Models

Different base obj choices for the Model are shown below The exact str indicator, as passed to the obj param is represented by the sub-heading (within “”) The avaliable models are further broken down by which can workwith different problem_types. Additionally, a link to the original models documentation as well as the implemented parameter distributions are shown.

binary

“dt classifier”

Base Class Documenation: sklearn.tree.DecisionTreeClassifier

Param Distributions

0. “default”

   defaults only
   

1. “dt classifier dist”

   max_depth: ng.p.Scalar(lower=1, upper=30).set_integer_casting()
   min_samples_split: ng.p.Scalar(lower=2, upper=50).set_integer_casting()
   class_weight: ng.p.TransitionChoice([None, 'balanced'])
   

“elastic net logistic”

Base Class Documenation: sklearn.linear_model.LogisticRegression

Param Distributions

0. “base elastic”

   max_iter: 1000
   multi_class: 'auto'
   penalty: 'elasticnet'
   class_weight: None
   solver: 'saga'
   l1_ratio: .5
   

1. “elastic classifier”

   max_iter: 1000
   multi_class: 'auto'
   penalty: 'elasticnet'
   class_weight: ng.p.TransitionChoice([None, 'balanced'])
   solver: 'saga'
   l1_ratio: ng.p.Scalar(lower=.01, upper=1)
   C: ng.p.Log(lower=1e-5, upper=1e5)
   

2. “elastic clf v2”

   max_iter: 1000
   multi_class: 'auto'
   penalty: 'elasticnet'
   class_weight: ng.p.TransitionChoice([None, 'balanced'])
   solver: 'saga'
   l1_ratio: ng.p.Scalar(lower=.01, upper=1)
   C: ng.p.Log(lower=1e-2, upper=1e5)
   

3. “elastic classifier extra”

   max_iter: ng.p.Scalar(lower=1000, upper=10000).set_integer_casting()
   multi_class: 'auto'
   penalty: 'elasticnet'
   class_weight: ng.p.TransitionChoice([None, 'balanced'])
   solver: 'saga'
   l1_ratio: ng.p.Scalar(lower=.01, upper=1)
   C: ng.p.Log(lower=1e-5, upper=1e5)
   tol: ng.p.Log(lower=1e-6, upper=.01)
   

“et classifier”

Base Class Documenation: sklearn.ensemble.ExtraTreesClassifier

Param Distributions

0. “default”

   defaults only
   

“gaussian nb”

Base Class Documenation: sklearn.naive_bayes.GaussianNB

Param Distributions

0. “base gnb”

   var_smoothing: 1e-9
   

“gb classifier”

Base Class Documenation: sklearn.ensemble.GradientBoostingClassifier

Param Distributions

0. “default”

   defaults only
   

“gp classifier”

Base Class Documenation: sklearn.gaussian_process.GaussianProcessClassifier

Param Distributions

0. “base gp classifier”

   n_restarts_optimizer: 5
   

“hgb classifier”

Base Class Documenation: sklearn.ensemble.gradient_boosting.HistGradientBoostingClassifier

Param Distributions

0. “default”

   defaults only
   

“knn classifier”

Base Class Documenation: sklearn.neighbors.KNeighborsClassifier

Param Distributions

0. “base knn”

   n_neighbors: 5
   

1. “knn dist”

   weights: ng.p.TransitionChoice(['uniform', 'distance'])
   n_neighbors: ng.p.Scalar(lower=2, upper=25).set_integer_casting()
   

“lasso logistic”

Base Class Documenation: sklearn.linear_model.LogisticRegression

Param Distributions

0. “base lasso”

   max_iter: 1000
   multi_class: 'auto'
   penalty: 'l1'
   class_weight: None
   solver: 'liblinear'
   

1. “lasso C”

   max_iter: 1000
   multi_class: 'auto'
   penalty: 'l1'
   class_weight: ng.p.TransitionChoice([None, 'balanced'])
   solver: 'liblinear'
   C: ng.p.Log(lower=1e-5, upper=1e3)
   

2. “lasso C extra”

   max_iter: ng.p.Scalar(lower=1000, upper=10000).set_integer_casting()
   multi_class: 'auto'
   penalty: 'l1'
   class_weight: ng.p.TransitionChoice([None, 'balanced'])
   solver: 'liblinear'
   C: ng.p.Log(lower=1e-5, upper=1e3)
   tol: ng.p.Log(lower=1e-6, upper=.01)
   

“light gbm classifier”

Base Class Documenation: lightgbm.LGBMClassifier

Param Distributions

0. “base lgbm”

   silent: True
   

1. “lgbm classifier dist1”

   silent: True
   boosting_type: ng.p.TransitionChoice(['gbdt', 'dart', 'goss'])
   n_estimators: ng.p.Scalar(init=100, lower=3, upper=500).set_integer_casting()
   num_leaves: ng.p.Scalar(init=20, lower=6, upper=80).set_integer_casting()
   min_child_samples: ng.p.Scalar(lower=10, upper=500).set_integer_casting()
   min_child_weight: ng.p.Log(lower=1e-5, upper=1e4)
   subsample: ng.p.Scalar(lower=.3, upper=.95)
   colsample_bytree: ng.p.Scalar(lower=.3, upper=.95)
   reg_alpha: ng.p.TransitionChoice([0, ng.p.Log(lower=1e-5, upper=1)])
   reg_lambda: ng.p.TransitionChoice([0, ng.p.Log(lower=1e-5, upper=1)])
   class_weight: ng.p.TransitionChoice([None, 'balanced'])
   

2. “lgbm classifier dist2”

   silent: True
   lambda_l2: 0.001
   boosting_type: ng.p.TransitionChoice(['gbdt', 'dart'])
   min_child_samples: ng.p.TransitionChoice([1, 5, 7, 10, 15, 20, 35, 50, 100, 200, 500, 1000])
   num_leaves: ng.p.TransitionChoice([2, 4, 7, 10, 15, 20, 25, 30, 35, 40, 50, 65, 80, 100, 125, 150, 200, 250])
   colsample_bytree: ng.p.TransitionChoice([0.7, 0.9, 1.0])
   subsample: ng.p.Scalar(lower=.3, upper=1)
   learning_rate: ng.p.TransitionChoice([0.01, 0.05, 0.1])
   n_estimators: ng.p.TransitionChoice([5, 20, 35, 50, 75, 100, 150, 200, 350, 500, 750, 1000])
   class_weight: ng.p.TransitionChoice([None, 'balanced'])
   

“linear svm classifier”

Base Class Documenation: sklearn.svm.LinearSVC

Param Distributions

0. “base linear svc”

   max_iter: 1000
   

1. “linear svc dist”

   max_iter: 1000
   C: ng.p.Log(lower=1e-4, upper=1e4)
   class_weight: ng.p.TransitionChoice([None, 'balanced'])
   

“logistic”

Base Class Documenation: sklearn.linear_model.LogisticRegression

Param Distributions

0. “base logistic”

   max_iter: 1000
   multi_class: 'auto'
   penalty: 'none'
   class_weight: None
   solver: 'lbfgs'
   

“mlp classifier”

Base Class Documenation: BPt.extensions.MLP.MLPClassifier_Wrapper

Param Distributions

0. “default”

   defaults only
   

1. “mlp dist 3 layer”

   hidden_layer_sizes: ng.p.Array(init=(100, 100, 100)).set_mutation(sigma=50).set_bounds(lower=1, upper=300).set_integer_casting()
   activation: ng.p.TransitionChoice(['identity', 'logistic', 'tanh', 'relu'])
   alpha: ng.p.Log(lower=1e-5, upper=1e2)
   batch_size: ng.p.TransitionChoice(['auto', ng.p.Scalar(init=200, lower=50, upper=400).set_integer_casting()])
   learning_rate: ng.p.TransitionChoice(['constant', 'invscaling', 'adaptive'])
   learning_rate_init: ng.p.Log(lower=1e-5, upper=1e2)
   max_iter: ng.p.Scalar(init=200, lower=100, upper=1000).set_integer_casting()
   beta_1: ng.p.Scalar(init=.9, lower=.1, upper=.99)
   beta_2: ng.p.Scalar(init=.999, lower=.1, upper=.9999)
   

2. “mlp dist es 3 layer”

   hidden_layer_sizes: ng.p.Scalar(init=100, lower=2, upper=300).set_integer_casting()
   activation: ng.p.TransitionChoice(['identity', 'logistic', 'tanh', 'relu'])
   alpha: ng.p.Log(lower=1e-5, upper=1e2)
   batch_size: ng.p.TransitionChoice(['auto', ng.p.Scalar(init=200, lower=50, upper=400).set_integer_casting()])
   learning_rate: ng.p.TransitionChoice(['constant', 'invscaling', 'adaptive'])
   learning_rate_init: ng.p.Log(lower=1e-5, upper=1e2)
   max_iter: ng.p.Scalar(init=200, lower=100, upper=1000).set_integer_casting()
   beta_1: ng.p.Scalar(init=.9, lower=.1, upper=.99)
   beta_2: ng.p.Scalar(init=.999, lower=.1, upper=.9999)
   early_stopping: True
   n_iter_no_change: ng.p.Scalar(lower=5, upper=50)
   

3. “mlp dist 2 layer”

   hidden_layer_sizes: ng.p.Array(init=(100, 100)).set_mutation(sigma=50).set_bounds(lower=1, upper=300).set_integer_casting()
   activation: ng.p.TransitionChoice(['identity', 'logistic', 'tanh', 'relu'])
   alpha: ng.p.Log(lower=1e-5, upper=1e2)
   batch_size: ng.p.TransitionChoice(['auto', ng.p.Scalar(init=200, lower=50, upper=400).set_integer_casting()])
   learning_rate: ng.p.TransitionChoice(['constant', 'invscaling', 'adaptive'])
   learning_rate_init: ng.p.Log(lower=1e-5, upper=1e2)
   max_iter: ng.p.Scalar(init=200, lower=100, upper=1000).set_integer_casting()
   beta_1: ng.p.Scalar(init=.9, lower=.1, upper=.99)
   beta_2: ng.p.Scalar(init=.999, lower=.1, upper=.9999)
   

4. “mlp dist es 2 layer”

   hidden_layer_sizes: ng.p.Scalar(init=100, lower=2, upper=300).set_integer_casting()
   activation: ng.p.TransitionChoice(['identity', 'logistic', 'tanh', 'relu'])
   alpha: ng.p.Log(lower=1e-5, upper=1e2)
   batch_size: ng.p.TransitionChoice(['auto', ng.p.Scalar(init=200, lower=50, upper=400).set_integer_casting()])
   learning_rate: ng.p.TransitionChoice(['constant', 'invscaling', 'adaptive'])
   learning_rate_init: ng.p.Log(lower=1e-5, upper=1e2)
   max_iter: ng.p.Scalar(init=200, lower=100, upper=1000).set_integer_casting()
   beta_1: ng.p.Scalar(init=.9, lower=.1, upper=.99)
   beta_2: ng.p.Scalar(init=.999, lower=.1, upper=.9999)
   early_stopping: True
   n_iter_no_change: ng.p.Scalar(lower=5, upper=50)
   

5. “mlp dist 1 layer”

   hidden_layer_sizes: ng.p.Scalar(init=100, lower=2, upper=300).set_integer_casting()
   activation: ng.p.TransitionChoice(['identity', 'logistic', 'tanh', 'relu'])
   alpha: ng.p.Log(lower=1e-5, upper=1e2)
   batch_size: ng.p.TransitionChoice(['auto', ng.p.Scalar(init=200, lower=50, upper=400).set_integer_casting()])
   learning_rate: ng.p.TransitionChoice(['constant', 'invscaling', 'adaptive'])
   learning_rate_init: ng.p.Log(lower=1e-5, upper=1e2)
   max_iter: ng.p.Scalar(init=200, lower=100, upper=1000).set_integer_casting()
   beta_1: ng.p.Scalar(init=.9, lower=.1, upper=.99)
   beta_2: ng.p.Scalar(init=.999, lower=.1, upper=.9999)
   

6. “mlp dist es 1 layer”

   hidden_layer_sizes: ng.p.Scalar(init=100, lower=2, upper=300).set_integer_casting()
   activation: ng.p.TransitionChoice(['identity', 'logistic', 'tanh', 'relu'])
   alpha: ng.p.Log(lower=1e-5, upper=1e2)
   batch_size: ng.p.TransitionChoice(['auto', ng.p.Scalar(init=200, lower=50, upper=400).set_integer_casting()])
   learning_rate: ng.p.TransitionChoice(['constant', 'invscaling', 'adaptive'])
   learning_rate_init: ng.p.Log(lower=1e-5, upper=1e2)
   max_iter: ng.p.Scalar(init=200, lower=100, upper=1000).set_integer_casting()
   beta_1: ng.p.Scalar(init=.9, lower=.1, upper=.99)
   beta_2: ng.p.Scalar(init=.999, lower=.1, upper=.9999)
   early_stopping: True
   n_iter_no_change: ng.p.Scalar(lower=5, upper=50)
   

“pa classifier”

Base Class Documenation: sklearn.linear_model.PassiveAggressiveClassifier

Param Distributions

0. “default”

   defaults only
   

“random forest classifier”

Base Class Documenation: sklearn.ensemble.RandomForestClassifier

Param Distributions

0. “base rf regressor”

   n_estimators: 100
   

1. “rf classifier dist”

   n_estimators: ng.p.Scalar(init=100, lower=3, upper=500).set_integer_casting()
   max_depth: ng.p.TransitionChoice([None, ng.p.Scalar(init=25, lower=2, upper=200).set_integer_casting()])
   max_features: ng.p.Scalar(lower=.1, upper=1.0)
   min_samples_split: ng.p.Scalar(lower=.1, upper=1.0)
   bootstrap: True
   class_weight: ng.p.TransitionChoice([None, 'balanced'])
   

“ridge logistic”

Base Class Documenation: sklearn.linear_model.LogisticRegression

Param Distributions

0. “base ridge”

   max_iter: 1000
   penalty: 'l2'
   solver: 'saga'
   

1. “ridge C”

   max_iter: 1000
   solver: 'saga'
   C: ng.p.Log(lower=1e-5, upper=1e3)
   class_weight: ng.p.TransitionChoice([None, 'balanced'])
   

2. “ridge C extra”

   max_iter: ng.p.Scalar(lower=1000, upper=10000).set_integer_casting()
   solver: 'saga'
   C: ng.p.Log(lower=1e-5, upper=1e3)
   class_weight: ng.p.TransitionChoice([None, 'balanced'])
   tol: ng.p.Log(lower=1e-6, upper=.01)
   

“sgd classifier”

Base Class Documenation: sklearn.linear_model.SGDClassifier

Param Distributions

0. “base sgd”

   loss: 'hinge'
   

1. “sgd classifier”

   loss: ng.p.TransitionChoice(['hinge', 'log', 'modified_huber', 'squared_hinge', 'perceptron'])
   penalty: ng.p.TransitionChoice(['l2', 'l1', 'elasticnet'])
   alpha: ng.p.Log(lower=1e-5, upper=1e2)
   l1_ratio: ng.p.Scalar(lower=0, upper=1)
   max_iter: 1000
   learning_rate: ng.p.TransitionChoice(['optimal', 'invscaling', 'adaptive', 'constant'])
   eta0: ng.p.Log(lower=1e-6, upper=1e3)
   power_t: ng.p.Scalar(lower=.1, upper=.9)
   early_stopping: ng.p.TransitionChoice([False, True])
   validation_fraction: ng.p.Scalar(lower=.05, upper=.5)
   n_iter_no_change: ng.p.TransitionChoice(np.arange(2, 20))
   class_weight: ng.p.TransitionChoice([None, 'balanced'])
   

“svm classifier”

Base Class Documenation: sklearn.svm.SVC

Param Distributions

0. “base svm classifier”

   kernel: 'rbf'
   gamma: 'scale'
   probability: True
   

1. “svm classifier dist”

   kernel: 'rbf'
   gamma: ng.p.Log(lower=1e-6, upper=1)
   C: ng.p.Log(lower=1e-4, upper=1e4)
   probability: True
   class_weight: ng.p.TransitionChoice([None, 'balanced'])
   

“xgb classifier”

Base Class Documenation: xgboost.XGBClassifier

Param Distributions

0. “base xgb classifier”

   verbosity: 0
   objective: 'binary:logistic'
   

1. “xgb classifier dist1”

   verbosity: 0
   objective: 'binary:logistic'
   n_estimators: ng.p.Scalar(init=100, lower=3, upper=500).set_integer_casting()
   min_child_weight: ng.p.Log(lower=1e-5, upper=1e4)
   subsample: ng.p.Scalar(lower=.3, upper=.95)
   colsample_bytree: ng.p.Scalar(lower=.3, upper=.95)
   reg_alpha: ng.p.TransitionChoice([0, ng.p.Log(lower=1e-5, upper=1)])
   reg_lambda: ng.p.TransitionChoice([0, ng.p.Log(lower=1e-5, upper=1)])
   

2. “xgb classifier dist2”

   verbosity: 0
   objective: 'binary:logistic'
   max_depth: ng.p.TransitionChoice([None, ng.p.Scalar(init=25, lower=2, upper=200).set_integer_casting()])
   learning_rate: ng.p.Scalar(lower=.01, upper=.5)
   n_estimators: ng.p.Scalar(lower=3, upper=500).set_integer_casting()
   min_child_weight: ng.p.TransitionChoice([1, 5, 10, 50])
   subsample: ng.p.Scalar(lower=.5, upper=1)
   colsample_bytree: ng.p.Scalar(lower=.4, upper=.95)
   

3. “xgb classifier dist3”

   verbosity: 0
   objective: 'binary:logistic'
   learning_rare: ng.p.Scalar(lower=.005, upper=.3)
   min_child_weight: ng.p.Scalar(lower=.5, upper=10)
   max_depth: ng.p.TransitionChoice(np.arange(3, 10))
   subsample: ng.p.Scalar(lower=.5, upper=1)
   colsample_bytree: ng.p.Scalar(lower=.5, upper=1)
   reg_alpha: ng.p.Log(lower=.00001, upper=1)
   

regression

“ard regressor”

Base Class Documenation: sklearn.linear_model.ARDRegression

Param Distributions

0. “default”

   defaults only
   

“bayesian ridge regressor”

Base Class Documenation: sklearn.linear_model.BayesianRidge

Param Distributions

0. “default”

   defaults only
   

“dt regressor”

Base Class Documenation: sklearn.tree.DecisionTreeRegressor

Param Distributions

0. “default”

   defaults only
   

1. “dt dist”

   max_depth: ng.p.Scalar(lower=1, upper=30).set_integer_casting()
   min_samples_split: ng.p.Scalar(lower=2, upper=50).set_integer_casting()
   

“elastic net regressor”

Base Class Documenation: sklearn.linear_model.ElasticNet

Param Distributions

0. “base elastic net”

   max_iter: 1000
   

1. “elastic regression”

   max_iter: 1000
   alpha: ng.p.Log(lower=1e-5, upper=1e5)
   l1_ratio: ng.p.Scalar(lower=.01, upper=1)
   

2. “elastic regression extra”

   max_iter: ng.p.Scalar(lower=1000, upper=10000).set_integer_casting()
   alpha: ng.p.Log(lower=1e-5, upper=1e5)
   l1_ratio: ng.p.Scalar(lower=.01, upper=1)
   tol: ng.p.Log(lower=1e-6, upper=.01)
   

“et regressor”

Base Class Documenation: sklearn.ensemble.ExtraTreesRegressor

Param Distributions

0. “default”

   defaults only
   

“gb regressor”

Base Class Documenation: sklearn.ensemble.GradientBoostingRegressor

Param Distributions

0. “default”

   defaults only
   

“gp regressor”

Base Class Documenation: sklearn.gaussian_process.GaussianProcessRegressor

Param Distributions

0. “base gp regressor”

   n_restarts_optimizer: 5
   normalize_y: True
   

“hgb regressor”

Base Class Documenation: sklearn.ensemble.gradient_boosting.HistGradientBoostingRegressor

Param Distributions

0. “default”

   defaults only
   

“knn regressor”

Base Class Documenation: sklearn.neighbors.KNeighborsRegressor

Param Distributions

0. “base knn regression”

   n_neighbors: 5
   

1. “knn dist regression”

   weights: ng.p.TransitionChoice(['uniform', 'distance'])
   n_neighbors: ng.p.Scalar(lower=2, upper=25).set_integer_casting()
   

“lasso regressor”

Base Class Documenation: sklearn.linear_model.Lasso

Param Distributions

0. “base lasso regressor”

   max_iter: 1000
   

1. “lasso regressor dist”

   max_iter: 1000
   alpha: ng.p.Log(lower=1e-5, upper=1e5)
   

“light gbm regressor”

Base Class Documenation: lightgbm.LGBMRegressor

Param Distributions

0. “base lgbm”

   silent: True
   

1. “lgbm dist1”

   silent: True
   boosting_type: ng.p.TransitionChoice(['gbdt', 'dart', 'goss'])
   n_estimators: ng.p.Scalar(init=100, lower=3, upper=500).set_integer_casting()
   num_leaves: ng.p.Scalar(init=20, lower=6, upper=80).set_integer_casting()
   min_child_samples: ng.p.Scalar(lower=10, upper=500).set_integer_casting()
   min_child_weight: ng.p.Log(lower=1e-5, upper=1e4)
   subsample: ng.p.Scalar(lower=.3, upper=.95)
   colsample_bytree: ng.p.Scalar(lower=.3, upper=.95)
   reg_alpha: ng.p.TransitionChoice([0, ng.p.Log(lower=1e-5, upper=1)])
   reg_lambda: ng.p.TransitionChoice([0, ng.p.Log(lower=1e-5, upper=1)])
   

2. “lgbm dist2”

   silent: True
   lambda_l2: 0.001
   boosting_type: ng.p.TransitionChoice(['gbdt', 'dart'])
   min_child_samples: ng.p.TransitionChoice([1, 5, 7, 10, 15, 20, 35, 50, 100, 200, 500, 1000])
   num_leaves: ng.p.TransitionChoice([2, 4, 7, 10, 15, 20, 25, 30, 35, 40, 50, 65, 80, 100, 125, 150, 200, 250])
   colsample_bytree: ng.p.TransitionChoice([0.7, 0.9, 1.0])
   subsample: ng.p.Scalar(lower=.3, upper=1)
   learning_rate: ng.p.TransitionChoice([0.01, 0.05, 0.1])
   n_estimators: ng.p.TransitionChoice([5, 20, 35, 50, 75, 100, 150, 200, 350, 500, 750, 1000])
   

“linear regressor”

Base Class Documenation: sklearn.linear_model.LinearRegression

Param Distributions

0. “base linear”

   fit_intercept: True
   

“linear svm regressor”

Base Class Documenation: sklearn.svm.LinearSVR

Param Distributions

0. “base linear svr”

   loss: 'epsilon_insensitive'
   max_iter: 1000
   

1. “linear svr dist”

   loss: 'epsilon_insensitive'
   max_iter: 1000
   C: ng.p.Log(lower=1e-4, upper=1e4)
   

“mlp regressor”

Base Class Documenation: BPt.extensions.MLP.MLPRegressor_Wrapper

Param Distributions

0. “default”

   defaults only
   

1. “mlp dist 3 layer”

   hidden_layer_sizes: ng.p.Array(init=(100, 100, 100)).set_mutation(sigma=50).set_bounds(lower=1, upper=300).set_integer_casting()
   activation: ng.p.TransitionChoice(['identity', 'logistic', 'tanh', 'relu'])
   alpha: ng.p.Log(lower=1e-5, upper=1e2)
   batch_size: ng.p.TransitionChoice(['auto', ng.p.Scalar(init=200, lower=50, upper=400).set_integer_casting()])
   learning_rate: ng.p.TransitionChoice(['constant', 'invscaling', 'adaptive'])
   learning_rate_init: ng.p.Log(lower=1e-5, upper=1e2)
   max_iter: ng.p.Scalar(init=200, lower=100, upper=1000).set_integer_casting()
   beta_1: ng.p.Scalar(init=.9, lower=.1, upper=.99)
   beta_2: ng.p.Scalar(init=.999, lower=.1, upper=.9999)
   

2. “mlp dist es 3 layer”

   hidden_layer_sizes: ng.p.Scalar(init=100, lower=2, upper=300).set_integer_casting()
   activation: ng.p.TransitionChoice(['identity', 'logistic', 'tanh', 'relu'])
   alpha: ng.p.Log(lower=1e-5, upper=1e2)
   batch_size: ng.p.TransitionChoice(['auto', ng.p.Scalar(init=200, lower=50, upper=400).set_integer_casting()])
   learning_rate: ng.p.TransitionChoice(['constant', 'invscaling', 'adaptive'])
   learning_rate_init: ng.p.Log(lower=1e-5, upper=1e2)
   max_iter: ng.p.Scalar(init=200, lower=100, upper=1000).set_integer_casting()
   beta_1: ng.p.Scalar(init=.9, lower=.1, upper=.99)
   beta_2: ng.p.Scalar(init=.999, lower=.1, upper=.9999)
   early_stopping: True
   n_iter_no_change: ng.p.Scalar(lower=5, upper=50)
   

3. “mlp dist 2 layer”

   hidden_layer_sizes: ng.p.Array(init=(100, 100)).set_mutation(sigma=50).set_bounds(lower=1, upper=300).set_integer_casting()
   activation: ng.p.TransitionChoice(['identity', 'logistic', 'tanh', 'relu'])
   alpha: ng.p.Log(lower=1e-5, upper=1e2)
   batch_size: ng.p.TransitionChoice(['auto', ng.p.Scalar(init=200, lower=50, upper=400).set_integer_casting()])
   learning_rate: ng.p.TransitionChoice(['constant', 'invscaling', 'adaptive'])
   learning_rate_init: ng.p.Log(lower=1e-5, upper=1e2)
   max_iter: ng.p.Scalar(init=200, lower=100, upper=1000).set_integer_casting()
   beta_1: ng.p.Scalar(init=.9, lower=.1, upper=.99)
   beta_2: ng.p.Scalar(init=.999, lower=.1, upper=.9999)
   

4. “mlp dist es 2 layer”

   hidden_layer_sizes: ng.p.Scalar(init=100, lower=2, upper=300).set_integer_casting()
   activation: ng.p.TransitionChoice(['identity', 'logistic', 'tanh', 'relu'])
   alpha: ng.p.Log(lower=1e-5, upper=1e2)
   batch_size: ng.p.TransitionChoice(['auto', ng.p.Scalar(init=200, lower=50, upper=400).set_integer_casting()])
   learning_rate: ng.p.TransitionChoice(['constant', 'invscaling', 'adaptive'])
   learning_rate_init: ng.p.Log(lower=1e-5, upper=1e2)
   max_iter: ng.p.Scalar(init=200, lower=100, upper=1000).set_integer_casting()
   beta_1: ng.p.Scalar(init=.9, lower=.1, upper=.99)
   beta_2: ng.p.Scalar(init=.999, lower=.1, upper=.9999)
   early_stopping: True
   n_iter_no_change: ng.p.Scalar(lower=5, upper=50)
   

5. “mlp dist 1 layer”

   hidden_layer_sizes: ng.p.Scalar(init=100, lower=2, upper=300).set_integer_casting()
   activation: ng.p.TransitionChoice(['identity', 'logistic', 'tanh', 'relu'])
   alpha: ng.p.Log(lower=1e-5, upper=1e2)
   batch_size: ng.p.TransitionChoice(['auto', ng.p.Scalar(init=200, lower=50, upper=400).set_integer_casting()])
   learning_rate: ng.p.TransitionChoice(['constant', 'invscaling', 'adaptive'])
   learning_rate_init: ng.p.Log(lower=1e-5, upper=1e2)
   max_iter: ng.p.Scalar(init=200, lower=100, upper=1000).set_integer_casting()
   beta_1: ng.p.Scalar(init=.9, lower=.1, upper=.99)
   beta_2: ng.p.Scalar(init=.999, lower=.1, upper=.9999)
   

6. “mlp dist es 1 layer”

   hidden_layer_sizes: ng.p.Scalar(init=100, lower=2, upper=300).set_integer_casting()
   activation: ng.p.TransitionChoice(['identity', 'logistic', 'tanh', 'relu'])
   alpha: ng.p.Log(lower=1e-5, upper=1e2)
   batch_size: ng.p.TransitionChoice(['auto', ng.p.Scalar(init=200, lower=50, upper=400).set_integer_casting()])
   learning_rate: ng.p.TransitionChoice(['constant', 'invscaling', 'adaptive'])
   learning_rate_init: ng.p.Log(lower=1e-5, upper=1e2)
   max_iter: ng.p.Scalar(init=200, lower=100, upper=1000).set_integer_casting()
   beta_1: ng.p.Scalar(init=.9, lower=.1, upper=.99)
   beta_2: ng.p.Scalar(init=.999, lower=.1, upper=.9999)
   early_stopping: True
   n_iter_no_change: ng.p.Scalar(lower=5, upper=50)
   

“random forest regressor”

Base Class Documenation: sklearn.ensemble.RandomForestRegressor

Param Distributions

0. “base rf”

   n_estimators: 100
   

1. “rf dist”

   n_estimators: ng.p.Scalar(init=100, lower=3, upper=500).set_integer_casting()
   max_depth: ng.p.TransitionChoice([None, ng.p.Scalar(init=25, lower=2, upper=200).set_integer_casting()])
   max_features: ng.p.Scalar(lower=.1, upper=1.0)
   min_samples_split: ng.p.Scalar(lower=.1, upper=1.0)
   bootstrap: True
   

“ridge regressor”

Base Class Documenation: sklearn.linear_model.Ridge

Param Distributions

0. “base ridge regressor”

   max_iter: 1000
   solver: 'lsqr'
   

1. “ridge regressor dist”

   max_iter: 1000
   solver: 'lsqr'
   alpha: ng.p.Log(lower=1e-3, upper=1e5)
   

“svm regressor”

Base Class Documenation: sklearn.svm.SVR

Param Distributions

0. “base svm”

   kernel: 'rbf'
   gamma: 'scale'
   

1. “svm dist”

   kernel: 'rbf'
   gamma: ng.p.Log(lower=1e-6, upper=1)
   C: ng.p.Log(lower=1e-4, upper=1e4)
   

“tweedie regressor”

Base Class Documenation: sklearn.linear_model.glm.TweedieRegressor

Param Distributions

0. “default”

   defaults only
   

“xgb regressor”

Base Class Documenation: xgboost.XGBRegressor

Param Distributions

0. “base xgb”

   verbosity: 0
   objective: 'reg:squarederror'
   

1. “xgb dist1”

   verbosity: 0
   objective: 'reg:squarederror'
   n_estimators: ng.p.Scalar(init=100, lower=3, upper=500).set_integer_casting()
   min_child_weight: ng.p.Log(lower=1e-5, upper=1e4)
   subsample: ng.p.Scalar(lower=.3, upper=.95)
   colsample_bytree: ng.p.Scalar(lower=.3, upper=.95)
   reg_alpha: ng.p.TransitionChoice([0, ng.p.Log(lower=1e-5, upper=1)])
   reg_lambda: ng.p.TransitionChoice([0, ng.p.Log(lower=1e-5, upper=1)])
   

2. “xgb dist2”

   verbosity: 0
   objective: 'reg:squarederror'
   max_depth: ng.p.TransitionChoice([None, ng.p.Scalar(init=25, lower=2, upper=200).set_integer_casting()])
   learning_rate: ng.p.Scalar(lower=.01, upper=.5)
   n_estimators: ng.p.Scalar(lower=3, upper=500).set_integer_casting()
   min_child_weight: ng.p.TransitionChoice([1, 5, 10, 50])
   subsample: ng.p.Scalar(lower=.5, upper=1)
   colsample_bytree: ng.p.Scalar(lower=.4, upper=.95)
   

3. “xgb dist3”

   verbosity: 0
   objective: 'reg:squarederror'
   learning_rare: ng.p.Scalar(lower=.005, upper=.3)
   min_child_weight: ng.p.Scalar(lower=.5, upper=10)
   max_depth: ng.p.TransitionChoice(np.arange(3, 10))
   subsample: ng.p.Scalar(lower=.5, upper=1)
   colsample_bytree: ng.p.Scalar(lower=.5, upper=1)
   reg_alpha: ng.p.Log(lower=.00001, upper=1)
   

categorical

“dt classifier”

Base Class Documenation: sklearn.tree.DecisionTreeClassifier

Param Distributions

0. “default”

   defaults only
   

1. “dt classifier dist”

   max_depth: ng.p.Scalar(lower=1, upper=30).set_integer_casting()
   min_samples_split: ng.p.Scalar(lower=2, upper=50).set_integer_casting()
   class_weight: ng.p.TransitionChoice([None, 'balanced'])
   

“elastic net logistic”

Base Class Documenation: sklearn.linear_model.LogisticRegression

Param Distributions

0. “base elastic”

   max_iter: 1000
   multi_class: 'auto'
   penalty: 'elasticnet'
   class_weight: None
   solver: 'saga'
   l1_ratio: .5
   

1. “elastic classifier”

   max_iter: 1000
   multi_class: 'auto'
   penalty: 'elasticnet'
   class_weight: ng.p.TransitionChoice([None, 'balanced'])
   solver: 'saga'
   l1_ratio: ng.p.Scalar(lower=.01, upper=1)
   C: ng.p.Log(lower=1e-5, upper=1e5)
   

2. “elastic clf v2”

   max_iter: 1000
   multi_class: 'auto'
   penalty: 'elasticnet'
   class_weight: ng.p.TransitionChoice([None, 'balanced'])
   solver: 'saga'
   l1_ratio: ng.p.Scalar(lower=.01, upper=1)
   C: ng.p.Log(lower=1e-2, upper=1e5)
   

3. “elastic classifier extra”

   max_iter: ng.p.Scalar(lower=1000, upper=10000).set_integer_casting()
   multi_class: 'auto'
   penalty: 'elasticnet'
   class_weight: ng.p.TransitionChoice([None, 'balanced'])
   solver: 'saga'
   l1_ratio: ng.p.Scalar(lower=.01, upper=1)
   C: ng.p.Log(lower=1e-5, upper=1e5)
   tol: ng.p.Log(lower=1e-6, upper=.01)
   

“et classifier”

Base Class Documenation: sklearn.ensemble.ExtraTreesClassifier

Param Distributions

0. “default”

   defaults only
   

“gaussian nb”

Base Class Documenation: sklearn.naive_bayes.GaussianNB

Param Distributions

0. “base gnb”

   var_smoothing: 1e-9
   

“gb classifier”

Base Class Documenation: sklearn.ensemble.GradientBoostingClassifier

Param Distributions

0. “default”

   defaults only
   

“gp classifier”

Base Class Documenation: sklearn.gaussian_process.GaussianProcessClassifier

Param Distributions

0. “base gp classifier”

   n_restarts_optimizer: 5
   

“hgb classifier”

Base Class Documenation: sklearn.ensemble.gradient_boosting.HistGradientBoostingClassifier

Param Distributions

0. “default”

   defaults only
   

“knn classifier”

Base Class Documenation: sklearn.neighbors.KNeighborsClassifier

Param Distributions

0. “base knn”

   n_neighbors: 5
   

1. “knn dist”

   weights: ng.p.TransitionChoice(['uniform', 'distance'])
   n_neighbors: ng.p.Scalar(lower=2, upper=25).set_integer_casting()
   

“lasso logistic”

Base Class Documenation: sklearn.linear_model.LogisticRegression

Param Distributions

0. “base lasso”

   max_iter: 1000
   multi_class: 'auto'
   penalty: 'l1'
   class_weight: None
   solver: 'liblinear'
   

1. “lasso C”

   max_iter: 1000
   multi_class: 'auto'
   penalty: 'l1'
   class_weight: ng.p.TransitionChoice([None, 'balanced'])
   solver: 'liblinear'
   C: ng.p.Log(lower=1e-5, upper=1e3)
   

2. “lasso C extra”

   max_iter: ng.p.Scalar(lower=1000, upper=10000).set_integer_casting()
   multi_class: 'auto'
   penalty: 'l1'
   class_weight: ng.p.TransitionChoice([None, 'balanced'])
   solver: 'liblinear'
   C: ng.p.Log(lower=1e-5, upper=1e3)
   tol: ng.p.Log(lower=1e-6, upper=.01)
   

“light gbm classifier”

Base Class Documenation: lightgbm.LGBMClassifier

Param Distributions

0. “base lgbm”

   silent: True
   

1. “lgbm classifier dist1”

   silent: True
   boosting_type: ng.p.TransitionChoice(['gbdt', 'dart', 'goss'])
   n_estimators: ng.p.Scalar(init=100, lower=3, upper=500).set_integer_casting()
   num_leaves: ng.p.Scalar(init=20, lower=6, upper=80).set_integer_casting()
   min_child_samples: ng.p.Scalar(lower=10, upper=500).set_integer_casting()
   min_child_weight: ng.p.Log(lower=1e-5, upper=1e4)
   subsample: ng.p.Scalar(lower=.3, upper=.95)
   colsample_bytree: ng.p.Scalar(lower=.3, upper=.95)
   reg_alpha: ng.p.TransitionChoice([0, ng.p.Log(lower=1e-5, upper=1)])
   reg_lambda: ng.p.TransitionChoice([0, ng.p.Log(lower=1e-5, upper=1)])
   class_weight: ng.p.TransitionChoice([None, 'balanced'])
   

2. “lgbm classifier dist2”

   silent: True
   lambda_l2: 0.001
   boosting_type: ng.p.TransitionChoice(['gbdt', 'dart'])
   min_child_samples: ng.p.TransitionChoice([1, 5, 7, 10, 15, 20, 35, 50, 100, 200, 500, 1000])
   num_leaves: ng.p.TransitionChoice([2, 4, 7, 10, 15, 20, 25, 30, 35, 40, 50, 65, 80, 100, 125, 150, 200, 250])
   colsample_bytree: ng.p.TransitionChoice([0.7, 0.9, 1.0])
   subsample: ng.p.Scalar(lower=.3, upper=1)
   learning_rate: ng.p.TransitionChoice([0.01, 0.05, 0.1])
   n_estimators: ng.p.TransitionChoice([5, 20, 35, 50, 75, 100, 150, 200, 350, 500, 750, 1000])
   class_weight: ng.p.TransitionChoice([None, 'balanced'])
   

“linear svm classifier”

Base Class Documenation: sklearn.svm.LinearSVC

Param Distributions

0. “base linear svc”

   max_iter: 1000
   

1. “linear svc dist”

   max_iter: 1000
   C: ng.p.Log(lower=1e-4, upper=1e4)
   class_weight: ng.p.TransitionChoice([None, 'balanced'])
   

“logistic”

Base Class Documenation: sklearn.linear_model.LogisticRegression

Param Distributions

0. “base logistic”

   max_iter: 1000
   multi_class: 'auto'
   penalty: 'none'
   class_weight: None
   solver: 'lbfgs'
   

“mlp classifier”

Base Class Documenation: BPt.extensions.MLP.MLPClassifier_Wrapper

Param Distributions

0. “default”

   defaults only
   

1. “mlp dist 3 layer”

   hidden_layer_sizes: ng.p.Array(init=(100, 100, 100)).set_mutation(sigma=50).set_bounds(lower=1, upper=300).set_integer_casting()
   activation: ng.p.TransitionChoice(['identity', 'logistic', 'tanh', 'relu'])
   alpha: ng.p.Log(lower=1e-5, upper=1e2)
   batch_size: ng.p.TransitionChoice(['auto', ng.p.Scalar(init=200, lower=50, upper=400).set_integer_casting()])
   learning_rate: ng.p.TransitionChoice(['constant', 'invscaling', 'adaptive'])
   learning_rate_init: ng.p.Log(lower=1e-5, upper=1e2)
   max_iter: ng.p.Scalar(init=200, lower=100, upper=1000).set_integer_casting()
   beta_1: ng.p.Scalar(init=.9, lower=.1, upper=.99)
   beta_2: ng.p.Scalar(init=.999, lower=.1, upper=.9999)
   

2. “mlp dist es 3 layer”

   hidden_layer_sizes: ng.p.Scalar(init=100, lower=2, upper=300).set_integer_casting()
   activation: ng.p.TransitionChoice(['identity', 'logistic', 'tanh', 'relu'])
   alpha: ng.p.Log(lower=1e-5, upper=1e2)
   batch_size: ng.p.TransitionChoice(['auto', ng.p.Scalar(init=200, lower=50, upper=400).set_integer_casting()])
   learning_rate: ng.p.TransitionChoice(['constant', 'invscaling', 'adaptive'])
   learning_rate_init: ng.p.Log(lower=1e-5, upper=1e2)
   max_iter: ng.p.Scalar(init=200, lower=100, upper=1000).set_integer_casting()
   beta_1: ng.p.Scalar(init=.9, lower=.1, upper=.99)
   beta_2: ng.p.Scalar(init=.999, lower=.1, upper=.9999)
   early_stopping: True
   n_iter_no_change: ng.p.Scalar(lower=5, upper=50)
   

3. “mlp dist 2 layer”

   hidden_layer_sizes: ng.p.Array(init=(100, 100)).set_mutation(sigma=50).set_bounds(lower=1, upper=300).set_integer_casting()
   activation: ng.p.TransitionChoice(['identity', 'logistic', 'tanh', 'relu'])
   alpha: ng.p.Log(lower=1e-5, upper=1e2)
   batch_size: ng.p.TransitionChoice(['auto', ng.p.Scalar(init=200, lower=50, upper=400).set_integer_casting()])
   learning_rate: ng.p.TransitionChoice(['constant', 'invscaling', 'adaptive'])
   learning_rate_init: ng.p.Log(lower=1e-5, upper=1e2)
   max_iter: ng.p.Scalar(init=200, lower=100, upper=1000).set_integer_casting()
   beta_1: ng.p.Scalar(init=.9, lower=.1, upper=.99)
   beta_2: ng.p.Scalar(init=.999, lower=.1, upper=.9999)
   

4. “mlp dist es 2 layer”

   hidden_layer_sizes: ng.p.Scalar(init=100, lower=2, upper=300).set_integer_casting()
   activation: ng.p.TransitionChoice(['identity', 'logistic', 'tanh', 'relu'])
   alpha: ng.p.Log(lower=1e-5, upper=1e2)
   batch_size: ng.p.TransitionChoice(['auto', ng.p.Scalar(init=200, lower=50, upper=400).set_integer_casting()])
   learning_rate: ng.p.TransitionChoice(['constant', 'invscaling', 'adaptive'])
   learning_rate_init: ng.p.Log(lower=1e-5, upper=1e2)
   max_iter: ng.p.Scalar(init=200, lower=100, upper=1000).set_integer_casting()
   beta_1: ng.p.Scalar(init=.9, lower=.1, upper=.99)
   beta_2: ng.p.Scalar(init=.999, lower=.1, upper=.9999)
   early_stopping: True
   n_iter_no_change: ng.p.Scalar(lower=5, upper=50)
   

5. “mlp dist 1 layer”

   hidden_layer_sizes: ng.p.Scalar(init=100, lower=2, upper=300).set_integer_casting()
   activation: ng.p.TransitionChoice(['identity', 'logistic', 'tanh', 'relu'])
   alpha: ng.p.Log(lower=1e-5, upper=1e2)
   batch_size: ng.p.TransitionChoice(['auto', ng.p.Scalar(init=200, lower=50, upper=400).set_integer_casting()])
   learning_rate: ng.p.TransitionChoice(['constant', 'invscaling', 'adaptive'])
   learning_rate_init: ng.p.Log(lower=1e-5, upper=1e2)
   max_iter: ng.p.Scalar(init=200, lower=100, upper=1000).set_integer_casting()
   beta_1: ng.p.Scalar(init=.9, lower=.1, upper=.99)
   beta_2: ng.p.Scalar(init=.999, lower=.1, upper=.9999)
   

6. “mlp dist es 1 layer”

   hidden_layer_sizes: ng.p.Scalar(init=100, lower=2, upper=300).set_integer_casting()
   activation: ng.p.TransitionChoice(['identity', 'logistic', 'tanh', 'relu'])
   alpha: ng.p.Log(lower=1e-5, upper=1e2)
   batch_size: ng.p.TransitionChoice(['auto', ng.p.Scalar(init=200, lower=50, upper=400).set_integer_casting()])
   learning_rate: ng.p.TransitionChoice(['constant', 'invscaling', 'adaptive'])
   learning_rate_init: ng.p.Log(lower=1e-5, upper=1e2)
   max_iter: ng.p.Scalar(init=200, lower=100, upper=1000).set_integer_casting()
   beta_1: ng.p.Scalar(init=.9, lower=.1, upper=.99)
   beta_2: ng.p.Scalar(init=.999, lower=.1, upper=.9999)
   early_stopping: True
   n_iter_no_change: ng.p.Scalar(lower=5, upper=50)
   

“pa classifier”

Base Class Documenation: sklearn.linear_model.PassiveAggressiveClassifier

Param Distributions

0. “default”

   defaults only
   

“random forest classifier”

Base Class Documenation: sklearn.ensemble.RandomForestClassifier

Param Distributions

0. “base rf regressor”

   n_estimators: 100
   

1. “rf classifier dist”

   n_estimators: ng.p.Scalar(init=100, lower=3, upper=500).set_integer_casting()
   max_depth: ng.p.TransitionChoice([None, ng.p.Scalar(init=25, lower=2, upper=200).set_integer_casting()])
   max_features: ng.p.Scalar(lower=.1, upper=1.0)
   min_samples_split: ng.p.Scalar(lower=.1, upper=1.0)
   bootstrap: True
   class_weight: ng.p.TransitionChoice([None, 'balanced'])
   

“ridge logistic”

Base Class Documenation: sklearn.linear_model.LogisticRegression

Param Distributions

0. “base ridge”

   max_iter: 1000
   penalty: 'l2'
   solver: 'saga'
   

1. “ridge C”

   max_iter: 1000
   solver: 'saga'
   C: ng.p.Log(lower=1e-5, upper=1e3)
   class_weight: ng.p.TransitionChoice([None, 'balanced'])
   

2. “ridge C extra”

   max_iter: ng.p.Scalar(lower=1000, upper=10000).set_integer_casting()
   solver: 'saga'
   C: ng.p.Log(lower=1e-5, upper=1e3)
   class_weight: ng.p.TransitionChoice([None, 'balanced'])
   tol: ng.p.Log(lower=1e-6, upper=.01)
   

“sgd classifier”

Base Class Documenation: sklearn.linear_model.SGDClassifier

Param Distributions

0. “base sgd”

   loss: 'hinge'
   

1. “sgd classifier”

   loss: ng.p.TransitionChoice(['hinge', 'log', 'modified_huber', 'squared_hinge', 'perceptron'])
   penalty: ng.p.TransitionChoice(['l2', 'l1', 'elasticnet'])
   alpha: ng.p.Log(lower=1e-5, upper=1e2)
   l1_ratio: ng.p.Scalar(lower=0, upper=1)
   max_iter: 1000
   learning_rate: ng.p.TransitionChoice(['optimal', 'invscaling', 'adaptive', 'constant'])
   eta0: ng.p.Log(lower=1e-6, upper=1e3)
   power_t: ng.p.Scalar(lower=.1, upper=.9)
   early_stopping: ng.p.TransitionChoice([False, True])
   validation_fraction: ng.p.Scalar(lower=.05, upper=.5)
   n_iter_no_change: ng.p.TransitionChoice(np.arange(2, 20))
   class_weight: ng.p.TransitionChoice([None, 'balanced'])
   

“svm classifier”

Base Class Documenation: sklearn.svm.SVC

Param Distributions

0. “base svm classifier”

   kernel: 'rbf'
   gamma: 'scale'
   probability: True
   

1. “svm classifier dist”

   kernel: 'rbf'
   gamma: ng.p.Log(lower=1e-6, upper=1)
   C: ng.p.Log(lower=1e-4, upper=1e4)
   probability: True
   class_weight: ng.p.TransitionChoice([None, 'balanced'])
   

“xgb classifier”

Base Class Documenation: xgboost.XGBClassifier

Param Distributions

0. “base xgb classifier”

   verbosity: 0
   objective: 'binary:logistic'
   

1. “xgb classifier dist1”

   verbosity: 0
   objective: 'binary:logistic'
   n_estimators: ng.p.Scalar(init=100, lower=3, upper=500).set_integer_casting()
   min_child_weight: ng.p.Log(lower=1e-5, upper=1e4)
   subsample: ng.p.Scalar(lower=.3, upper=.95)
   colsample_bytree: ng.p.Scalar(lower=.3, upper=.95)
   reg_alpha: ng.p.TransitionChoice([0, ng.p.Log(lower=1e-5, upper=1)])
   reg_lambda: ng.p.TransitionChoice([0, ng.p.Log(lower=1e-5, upper=1)])
   

2. “xgb classifier dist2”

   verbosity: 0
   objective: 'binary:logistic'
   max_depth: ng.p.TransitionChoice([None, ng.p.Scalar(init=25, lower=2, upper=200).set_integer_casting()])
   learning_rate: ng.p.Scalar(lower=.01, upper=.5)
   n_estimators: ng.p.Scalar(lower=3, upper=500).set_integer_casting()
   min_child_weight: ng.p.TransitionChoice([1, 5, 10, 50])
   subsample: ng.p.Scalar(lower=.5, upper=1)
   colsample_bytree: ng.p.Scalar(lower=.4, upper=.95)
   

3. “xgb classifier dist3”

   verbosity: 0
   objective: 'binary:logistic'
   learning_rare: ng.p.Scalar(lower=.005, upper=.3)
   min_child_weight: ng.p.Scalar(lower=.5, upper=10)
   max_depth: ng.p.TransitionChoice(np.arange(3, 10))
   subsample: ng.p.Scalar(lower=.5, upper=1)
   colsample_bytree: ng.p.Scalar(lower=.5, upper=1)
   reg_alpha: ng.p.Log(lower=.00001, upper=1)
   

Scorers

Different availible choices for the scorer parameter are shown below. scorer is accepted by Problem_Spec, Param_Search and Feat_Importance The str indicator for each scorer is represented bythe sub-heading (within “”) The avaliable scorers are further broken down by which can work with different problem_types. Additionally, a link to the original models documentation is shown.

binary

“accuracy”

Base Func Documenation: sklearn.metrics.accuracy_score()

“roc_auc”

Base Func Documenation: sklearn.metrics.roc_auc_score()

“roc_auc_ovr”

Base Func Documenation: sklearn.metrics.roc_auc_score()

“roc_auc_ovo”

Base Func Documenation: sklearn.metrics.roc_auc_score()

“roc_auc_ovr_weighted”

Base Func Documenation: sklearn.metrics.roc_auc_score()

“roc_auc_ovo_weighted”

Base Func Documenation: sklearn.metrics.roc_auc_score()

“balanced_accuracy”

Base Func Documenation: sklearn.metrics.balanced_accuracy_score()

“average_precision”

Base Func Documenation: sklearn.metrics.average_precision_score()

“neg_log_loss”

Base Func Documenation: sklearn.metrics.log_loss()

“neg_brier_score”

Base Func Documenation: sklearn.metrics.brier_score_loss()

“precision”

Base Func Documenation: sklearn.metrics.precision_score()

“precision_macro”

Base Func Documenation: sklearn.metrics.precision_score()

“precision_micro”

Base Func Documenation: sklearn.metrics.precision_score()

“precision_samples”

Base Func Documenation: sklearn.metrics.precision_score()

“precision_weighted”

Base Func Documenation: sklearn.metrics.precision_score()

“recall”

Base Func Documenation: sklearn.metrics.recall_score()

“recall_macro”

Base Func Documenation: sklearn.metrics.recall_score()

“recall_micro”

Base Func Documenation: sklearn.metrics.recall_score()

“recall_samples”

Base Func Documenation: sklearn.metrics.recall_score()

“recall_weighted”

Base Func Documenation: sklearn.metrics.recall_score()

“f1”

Base Func Documenation: sklearn.metrics.f1_score()

“f1_macro”

Base Func Documenation: sklearn.metrics.f1_score()

“f1_micro”

Base Func Documenation: sklearn.metrics.f1_score()

“f1_samples”

Base Func Documenation: sklearn.metrics.f1_score()

“f1_weighted”

Base Func Documenation: sklearn.metrics.f1_score()

“jaccard”

Base Func Documenation: sklearn.metrics.jaccard_score()

“jaccard_macro”

Base Func Documenation: sklearn.metrics.jaccard_score()

“jaccard_micro”

Base Func Documenation: sklearn.metrics.jaccard_score()

“jaccard_samples”

Base Func Documenation: sklearn.metrics.jaccard_score()

“jaccard_weighted”

Base Func Documenation: sklearn.metrics.jaccard_score()

“neg_hamming”

Base Func Documenation: sklearn.metrics.hamming_loss()

“matthews”

Base Func Documenation: sklearn.metrics.matthews_corrcoef()

“default”

Base Func Documenation: sklearn.metrics.roc_auc_score()

regression

“explained_variance”

Base Func Documenation: sklearn.metrics.explained_variance_score()

“explained_variance score”

Base Func Documenation: sklearn.metrics.explained_variance_score()

“r2”

Base Func Documenation: sklearn.metrics.r2_score()

“max_error”

Base Func Documenation: sklearn.metrics.max_error()

“neg_median_absolute_error”

Base Func Documenation: sklearn.metrics.median_absolute_error()

“median_absolute_error”

Base Func Documenation: sklearn.metrics.median_absolute_error()

“neg_mean_absolute_error”

Base Func Documenation: sklearn.metrics.mean_absolute_error()

“mean_absolute_error”

Base Func Documenation: sklearn.metrics.mean_absolute_error()

“neg_mean_squared_error”

Base Func Documenation: sklearn.metrics.mean_squared_error()

“mean_squared_error”

Base Func Documenation: sklearn.metrics.mean_squared_error()

“neg_mean_squared_log_error”

Base Func Documenation: sklearn.metrics.mean_squared_log_error()

“mean_squared_log_error”

Base Func Documenation: sklearn.metrics.mean_squared_log_error()

“neg_root_mean_squared_error”

Base Func Documenation: sklearn.metrics.mean_squared_error()

“root_mean_squared_error”

Base Func Documenation: sklearn.metrics.mean_squared_error()

“neg_mean_poisson_deviance”

Base Func Documenation: sklearn.metrics.mean_poisson_deviance()

“mean_poisson_deviance”

Base Func Documenation: sklearn.metrics.mean_poisson_deviance()

“neg_mean_gamma_deviance”

Base Func Documenation: sklearn.metrics.mean_gamma_deviance()

“mean_gamma_deviance”

Base Func Documenation: sklearn.metrics.mean_gamma_deviance()

“default”

Base Func Documenation: sklearn.metrics.r2_score()

categorical

“accuracy”

Base Func Documenation: sklearn.metrics.accuracy_score()

“roc_auc”

Base Func Documenation: sklearn.metrics.roc_auc_score()

“roc_auc_ovr”

Base Func Documenation: sklearn.metrics.roc_auc_score()

“roc_auc_ovo”

Base Func Documenation: sklearn.metrics.roc_auc_score()

“roc_auc_ovr_weighted”

Base Func Documenation: sklearn.metrics.roc_auc_score()

“roc_auc_ovo_weighted”

Base Func Documenation: sklearn.metrics.roc_auc_score()

“balanced_accuracy”

Base Func Documenation: sklearn.metrics.balanced_accuracy_score()

“average_precision”

Base Func Documenation: sklearn.metrics.average_precision_score()

“neg_log_loss”

Base Func Documenation: sklearn.metrics.log_loss()

“neg_brier_score”

Base Func Documenation: sklearn.metrics.brier_score_loss()

“precision”

Base Func Documenation: sklearn.metrics.precision_score()

“precision_macro”

Base Func Documenation: sklearn.metrics.precision_score()

“precision_micro”

Base Func Documenation: sklearn.metrics.precision_score()

“precision_samples”

Base Func Documenation: sklearn.metrics.precision_score()

“precision_weighted”

Base Func Documenation: sklearn.metrics.precision_score()

“recall”

Base Func Documenation: sklearn.metrics.recall_score()

“recall_macro”

Base Func Documenation: sklearn.metrics.recall_score()

“recall_micro”

Base Func Documenation: sklearn.metrics.recall_score()

“recall_samples”

Base Func Documenation: sklearn.metrics.recall_score()

“recall_weighted”

Base Func Documenation: sklearn.metrics.recall_score()

“f1”

Base Func Documenation: sklearn.metrics.f1_score()

“f1_macro”

Base Func Documenation: sklearn.metrics.f1_score()

“f1_micro”

Base Func Documenation: sklearn.metrics.f1_score()

“f1_samples”

Base Func Documenation: sklearn.metrics.f1_score()

“f1_weighted”

Base Func Documenation: sklearn.metrics.f1_score()

“jaccard”

Base Func Documenation: sklearn.metrics.jaccard_score()

“jaccard_macro”

Base Func Documenation: sklearn.metrics.jaccard_score()

“jaccard_micro”

Base Func Documenation: sklearn.metrics.jaccard_score()

“jaccard_samples”

Base Func Documenation: sklearn.metrics.jaccard_score()

“jaccard_weighted”

Base Func Documenation: sklearn.metrics.jaccard_score()

“neg_hamming”

Base Func Documenation: sklearn.metrics.hamming_loss()

“matthews”

Base Func Documenation: sklearn.metrics.matthews_corrcoef()

“default”

Base Func Documenation: sklearn.metrics.roc_auc_score()

Loaders

Different base obj choices for the Loader are shown below The exact str indicator, as passed to the obj param is represented by the sub-heading (within “”) Additionally, a link to the original models documentation as well as the implemented parameter distributions are shown.

All Problem Types

“identity”

Base Class Documenation: BPt.extensions.Loaders.Identity

Param Distributions

0. “default”

   defaults only
   

“surface rois”

Base Class Documenation: BPt.extensions.Loaders.SurfLabels

Param Distributions

0. “default”

   defaults only
   

“volume rois”

Base Class Documenation: nilearn.input_data.nifti_labels_masker.NiftiLabelsMasker

Param Distributions

0. “default”

   defaults only
   

“connectivity”

Base Class Documenation: BPt.extensions.Loaders.Connectivity

Param Distributions

0. “default”

   defaults only
   

Imputers

Different base obj choices for the Imputer are shown below The exact str indicator, as passed to the obj param is represented by the sub-heading (within “”) Additionally, a link to the original models documentation as well as the implemented parameter distributions are shown. Note that if the iterative imputer is requested, base_model must also be passed.

All Problem Types

“mean”

Base Class Documenation: sklearn.impute.SimpleImputer

Param Distributions

0. “mean imp”

   strategy: 'mean'
   

“median”

Base Class Documenation: sklearn.impute.SimpleImputer

Param Distributions

0. “median imp”

   strategy: 'median'
   

“most frequent”

Base Class Documenation: sklearn.impute.SimpleImputer

Param Distributions

0. “most freq imp”

   strategy: 'most_frequent'
   

“constant”

Base Class Documenation: sklearn.impute.SimpleImputer

Param Distributions

0. “constant imp”

   strategy: 'constant'
   

“iterative”

Base Class Documenation: sklearn.impute.IterativeImputer

Param Distributions

0. “iterative imp”

   initial_strategy: 'mean'
   skip_complete: True
   

Scalers

Different base obj choices for the Scaler are shown below The exact str indicator, as passed to the obj param is represented by the sub-heading (within “”) Additionally, a link to the original models documentation as well as the implemented parameter distributions are shown.

All Problem Types

“standard”

Base Class Documenation: sklearn.preprocessing.StandardScaler

Param Distributions

0. “base standard”

   with_mean: True
   with_std: True
   

“minmax”

Base Class Documenation: sklearn.preprocessing.MinMaxScaler

Param Distributions

0. “base minmax”

   feature_range: (0, 1)
   

“maxabs”

Base Class Documenation: sklearn.preprocessing.MaxAbsScaler

Param Distributions

0. “default”

   defaults only
   

“robust”

Base Class Documenation: sklearn.preprocessing.RobustScaler

Param Distributions

0. “base robust”

   quantile_range: (5, 95)
   

1. “robust gs”

   quantile_range: ng.p.TransitionChoice([(x, 100-x) for x in np.arange(1, 40)])
   

“yeo”

Base Class Documenation: sklearn.preprocessing.PowerTransformer

Param Distributions

0. “base yeo”

   method: 'yeo-johnson'
   standardize: True
   

“boxcox”

Base Class Documenation: sklearn.preprocessing.PowerTransformer

Param Distributions

0. “base boxcox”

   method: 'box-cox'
   standardize: True
   

“winsorize”

Base Class Documenation: BPt.extensions.Scalers.Winsorizer

Param Distributions

0. “base winsorize”

   quantile_range: (1, 99)
   

1. “winsorize gs”

   quantile_range: ng.p.TransitionChoice([(x, 100-x) for x in np.arange(1, 40)])
   

“quantile norm”

Base Class Documenation: sklearn.preprocessing.QuantileTransformer

Param Distributions

0. “base quant norm”

   output_distribution: 'normal'
   

“quantile uniform”

Base Class Documenation: sklearn.preprocessing.QuantileTransformer

Param Distributions

0. “base quant uniform”

   output_distribution: 'uniform'
   

“normalize”

Base Class Documenation: sklearn.preprocessing.Normalizer

Param Distributions

0. “default”

   defaults only
   

Transformers

Different base obj choices for the Transformer are shown below The exact str indicator, as passed to the obj param is represented by the sub-heading (within “”) Additionally, a link to the original models documentation as well as the implemented parameter distributions are shown.

All Problem Types

“pca”

Base Class Documenation: sklearn.decomposition.PCA

Param Distributions

0. “default”

   defaults only
   

1. “pca var search”

   n_components: ng.p.Scalar(init=.75, lower=.1, upper=.99)
   svd_solver: 'full'
   

“sparse pca”

Base Class Documenation: sklearn.decomposition.SparsePCA

Param Distributions

0. “default”

   defaults only
   

“mini batch sparse pca”

Base Class Documenation: sklearn.decomposition.MiniBatchSparsePCA

Param Distributions

0. “default”

   defaults only
   

“factor analysis”

Base Class Documenation: sklearn.decomposition.FactorAnalysis

Param Distributions

0. “default”

   defaults only
   

“dictionary learning”

Base Class Documenation: sklearn.decomposition.DictionaryLearning

Param Distributions

0. “default”

   defaults only
   

“mini batch dictionary learning”

Base Class Documenation: sklearn.decomposition.MiniBatchDictionaryLearning

Param Distributions

0. “default”

   defaults only
   

“fast ica”

Base Class Documenation: sklearn.decomposition.FastICA

Param Distributions

0. “default”

   defaults only
   

“incremental pca”

Base Class Documenation: sklearn.decomposition.IncrementalPCA

Param Distributions

0. “default”

   defaults only
   

“kernel pca”

Base Class Documenation: sklearn.decomposition.KernelPCA

Param Distributions

0. “default”

   defaults only
   

“nmf”

Base Class Documenation: sklearn.decomposition.NMF

Param Distributions

0. “default”

   defaults only
   

“truncated svd”

Base Class Documenation: sklearn.decomposition.TruncatedSVD

Param Distributions

0. “default”

   defaults only
   

“one hot encoder”

Base Class Documenation: sklearn.preprocessing.OneHotEncoder

Param Distributions

0. “ohe”

   sparse: False
   handle_unknown: 'ignore'
   

“backward difference encoder”

Base Class Documenation: category_encoders.backward_difference.BackwardDifferenceEncoder

Param Distributions

0. “default”

   defaults only
   

“binary encoder”

Base Class Documenation: category_encoders.binary.BinaryEncoder

Param Distributions

0. “default”

   defaults only
   

“cat boost encoder”

Base Class Documenation: category_encoders.cat_boost.CatBoostEncoder

Param Distributions

0. “default”

   defaults only
   

“helmert encoder”

Base Class Documenation: category_encoders.helmert.HelmertEncoder

Param Distributions

0. “default”

   defaults only
   

“james stein encoder”

Base Class Documenation: category_encoders.james_stein.JamesSteinEncoder

Param Distributions

0. “default”

   defaults only
   

“leave one out encoder”

Base Class Documenation: category_encoders.leave_one_out.LeaveOneOutEncoder

Param Distributions

0. “default”

   defaults only
   

“m estimate encoder”

Base Class Documenation: category_encoders.m_estimate.MEstimateEncoder

Param Distributions

0. “default”

   defaults only
   

“polynomial encoder”

Base Class Documenation: category_encoders.polynomial.PolynomialEncoder

Param Distributions

0. “default”

   defaults only
   

“sum encoder”

Base Class Documenation: category_encoders.sum_coding.SumEncoder

Param Distributions

0. “default”

   defaults only
   

“target encoder”

Base Class Documenation: category_encoders.target_encoder.TargetEncoder

Param Distributions

0. “default”

   defaults only
   

“woe encoder”

Base Class Documenation: category_encoders.woe.WOEEncoder

Param Distributions

0. “default”

   defaults only
   

Feat Selectors

Different base obj choices for the Feat_Selector are shown below The exact str indicator, as passed to the obj param is represented by the sub-heading (within “”) The avaliable feat selectors are further broken down by which can workwith different problem_types. Additionally, a link to the original models documentation as well as the implemented parameter distributions are shown.

binary

“rfe”

Base Class Documenation: sklearn.feature_selection.RFE

Param Distributions

0. “base rfe”

   n_features_to_select: None
   

1. “rfe num feats dist”

   n_features_to_select: ng.p.Scalar(init=.5, lower=.1, upper=.99)
   

“selector”

Base Class Documenation: BPt.extensions.Feat_Selectors.FeatureSelector

Param Distributions

0. “random”

   mask: 'sets as random features'
   

1. “searchable”

   mask: 'sets as hyperparameters'
   

“univariate selection c”

Base Class Documenation: sklearn.feature_selection.SelectPercentile

Param Distributions

0. “base univar fs classifier”

   score_func: f_classif
   percentile: 50
   

1. “univar fs classifier dist”

   score_func: f_classif
   percentile: ng.p.Scalar(init=50, lower=1, upper=99)
   

2. “univar fs classifier dist2”

   score_func: f_classif
   percentile: ng.p.Scalar(init=75, lower=50, upper=99)
   

“variance threshold”

Base Class Documenation: sklearn.feature_selection.VarianceThreshold

Param Distributions

0. “default”

   defaults only
   

regression

“rfe”

Base Class Documenation: sklearn.feature_selection.RFE

Param Distributions

0. “base rfe”

   n_features_to_select: None
   

1. “rfe num feats dist”

   n_features_to_select: ng.p.Scalar(init=.5, lower=.1, upper=.99)
   

“selector”

Base Class Documenation: BPt.extensions.Feat_Selectors.FeatureSelector

Param Distributions

0. “random”

   mask: 'sets as random features'
   

1. “searchable”

   mask: 'sets as hyperparameters'
   

“univariate selection r”

Base Class Documenation: sklearn.feature_selection.SelectPercentile

Param Distributions

0. “base univar fs regression”

   score_func: f_regression
   percentile: 50
   

1. “univar fs regression dist”

   score_func: f_regression
   percentile: ng.p.Scalar(init=50, lower=1, upper=99)
   

2. “univar fs regression dist2”

   score_func: f_regression
   percentile: ng.p.Scalar(init=75, lower=50, upper=99)
   

“variance threshold”

Base Class Documenation: sklearn.feature_selection.VarianceThreshold

Param Distributions

0. “default”

   defaults only
   

categorical

“rfe”

Base Class Documenation: sklearn.feature_selection.RFE

Param Distributions

0. “base rfe”

   n_features_to_select: None
   

1. “rfe num feats dist”

   n_features_to_select: ng.p.Scalar(init=.5, lower=.1, upper=.99)
   

“selector”

Base Class Documenation: BPt.extensions.Feat_Selectors.FeatureSelector

Param Distributions

0. “random”

   mask: 'sets as random features'
   

1. “searchable”

   mask: 'sets as hyperparameters'
   

“univariate selection c”

Base Class Documenation: sklearn.feature_selection.SelectPercentile

Param Distributions

0. “base univar fs classifier”

   score_func: f_classif
   percentile: 50
   

1. “univar fs classifier dist”

   score_func: f_classif
   percentile: ng.p.Scalar(init=50, lower=1, upper=99)
   

2. “univar fs classifier dist2”

   score_func: f_classif
   percentile: ng.p.Scalar(init=75, lower=50, upper=99)
   

“variance threshold”

Base Class Documenation: sklearn.feature_selection.VarianceThreshold

Param Distributions

0. “default”

   defaults only
   

Ensemble Types

Different base obj choices for the Ensemble are shown below The exact str indicator, as passed to the obj param is represented by the sub-heading (within “”) The avaliable ensembles are further broken down by which can workwith different problem_types. Additionally, a link to the original models documentation as well as the implemented parameter distributions are shown. Also note that ensemble require a few extra params! I.e., in general, all DESlib based ensemble need needs_split = True

binary

“adaboost classifier”

Base Class Documenation: sklearn.ensemble.AdaBoostClassifier

Param Distributions

0. “default”

   defaults only
   

“aposteriori”

Base Class Documenation: deslib.dcs.a_posteriori.APosteriori

Param Distributions

0. “default”

   defaults only
   

“apriori”

Base Class Documenation: deslib.dcs.a_priori.APriori

Param Distributions

0. “default”

   defaults only
   

“bagging classifier”

Base Class Documenation: sklearn.ensemble.BaggingClassifier

Param Distributions

0. “default”

   defaults only
   

“balanced bagging classifier”

Base Class Documenation: imblearn.ensemble.BalancedBaggingClassifier

Param Distributions

0. “default”

   defaults only
   

“des clustering”

Base Class Documenation: deslib.des.des_clustering.DESClustering

Param Distributions

0. “default”

   defaults only
   

“des knn”

Base Class Documenation: deslib.des.des_knn.DESKNN

Param Distributions

0. “default”

   defaults only
   

“deskl”

Base Class Documenation: deslib.des.probabilistic.DESKL

Param Distributions

0. “default”

   defaults only
   

“desmi”

Base Class Documenation: deslib.des.des_mi.DESMI

Param Distributions

0. “default”

   defaults only
   

“desp”

Base Class Documenation: deslib.des.des_p.DESP

Param Distributions

0. “default”

   defaults only
   

“exponential”

Base Class Documenation: deslib.des.probabilistic.Exponential

Param Distributions

0. “default”

   defaults only
   

“knop”

Base Class Documenation: deslib.des.knop.KNOP

Param Distributions

0. “default”

   defaults only
   

“knorae”

Base Class Documenation: deslib.des.knora_e.KNORAE

Param Distributions

0. “default”

   defaults only
   

“knrau”

Base Class Documenation: deslib.des.knora_u.KNORAU

Param Distributions

0. “default”

   defaults only
   

“lca”

Base Class Documenation: deslib.dcs.lca.LCA

Param Distributions

0. “default”

   defaults only
   

“logarithmic”

Base Class Documenation: deslib.des.probabilistic.Logarithmic

Param Distributions

0. “default”

   defaults only
   

“mcb”

Base Class Documenation: deslib.dcs.mcb.MCB

Param Distributions

0. “default”

   defaults only
   

“metades”

Base Class Documenation: deslib.des.meta_des.METADES

Param Distributions

0. “default”

   defaults only
   

“min dif”

Base Class Documenation: deslib.des.probabilistic.MinimumDifference

Param Distributions

0. “default”

   defaults only
   

“mla”

Base Class Documenation: deslib.dcs.mla.MLA

Param Distributions

0. “default”

   defaults only
   

“ola”

Base Class Documenation: deslib.dcs.ola.OLA

Param Distributions

0. “default”

   defaults only
   

“rank”

Base Class Documenation: deslib.dcs.rank.Rank

Param Distributions

0. “default”

   defaults only
   

“rrc”

Base Class Documenation: deslib.des.probabilistic.RRC

Param Distributions

0. “default”

   defaults only
   

“single best”

Base Class Documenation: deslib.static.single_best.SingleBest

Param Distributions

0. “default”

   defaults only
   

“stacked”

Base Class Documenation: deslib.static.stacked.StackedClassifier

Param Distributions

0. “default”

   defaults only
   

“stacking classifier”

Base Class Documenation: BPt.pipeline.Ensembles.BPtStackingClassifier

Param Distributions

0. “default”

   defaults only
   

“voting classifier”

Base Class Documenation: BPt.pipeline.Ensembles.BPtVotingClassifier

Param Distributions

0. “voting classifier”

   voting: 'soft'
   

regression

“adaboost regressor”

Base Class Documenation: sklearn.ensemble.AdaBoostRegressor

Param Distributions

0. “default”

   defaults only
   

“bagging regressor”

Base Class Documenation: sklearn.ensemble.BaggingRegressor

Param Distributions

0. “default”

   defaults only
   

“stacking regressor”

Base Class Documenation: BPt.pipeline.Ensembles.BPtStackingRegressor

Param Distributions

0. “default”

   defaults only
   

“voting regressor”

Base Class Documenation: BPt.pipeline.Ensembles.BPtVotingRegressor

Param Distributions

0. “default”

   defaults only
   

categorical

“adaboost classifier”

Base Class Documenation: sklearn.ensemble.AdaBoostClassifier

Param Distributions

0. “default”

   defaults only
   

“aposteriori”

Base Class Documenation: deslib.dcs.a_posteriori.APosteriori

Param Distributions

0. “default”

   defaults only
   

“apriori”

Base Class Documenation: deslib.dcs.a_priori.APriori

Param Distributions

0. “default”

   defaults only
   

“bagging classifier”

Base Class Documenation: sklearn.ensemble.BaggingClassifier

Param Distributions

0. “default”

   defaults only
   

“balanced bagging classifier”

Base Class Documenation: imblearn.ensemble.BalancedBaggingClassifier

Param Distributions

0. “default”

   defaults only
   

“des clustering”

Base Class Documenation: deslib.des.des_clustering.DESClustering

Param Distributions

0. “default”

   defaults only
   

“des knn”

Base Class Documenation: deslib.des.des_knn.DESKNN

Param Distributions

0. “default”

   defaults only
   

“deskl”

Base Class Documenation: deslib.des.probabilistic.DESKL

Param Distributions

0. “default”

   defaults only
   

“desmi”

Base Class Documenation: deslib.des.des_mi.DESMI

Param Distributions

0. “default”

   defaults only
   

“desp”

Base Class Documenation: deslib.des.des_p.DESP

Param Distributions

0. “default”

   defaults only
   

“exponential”

Base Class Documenation: deslib.des.probabilistic.Exponential

Param Distributions

0. “default”

   defaults only
   

“knop”

Base Class Documenation: deslib.des.knop.KNOP

Param Distributions

0. “default”

   defaults only
   

“knorae”

Base Class Documenation: deslib.des.knora_e.KNORAE

Param Distributions

0. “default”

   defaults only
   

“knrau”

Base Class Documenation: deslib.des.knora_u.KNORAU

Param Distributions

0. “default”

   defaults only
   

“lca”

Base Class Documenation: deslib.dcs.lca.LCA

Param Distributions

0. “default”

   defaults only
   

“logarithmic”

Base Class Documenation: deslib.des.probabilistic.Logarithmic

Param Distributions

0. “default”

   defaults only
   

“mcb”

Base Class Documenation: deslib.dcs.mcb.MCB

Param Distributions

0. “default”

   defaults only
   

“metades”

Base Class Documenation: deslib.des.meta_des.METADES

Param Distributions

0. “default”

   defaults only
   

“min dif”

Base Class Documenation: deslib.des.probabilistic.MinimumDifference

Param Distributions

0. “default”

   defaults only
   

“mla”

Base Class Documenation: deslib.dcs.mla.MLA

Param Distributions

0. “default”

   defaults only
   

“ola”

Base Class Documenation: deslib.dcs.ola.OLA

Param Distributions

0. “default”

   defaults only
   

“rank”

Base Class Documenation: deslib.dcs.rank.Rank

Param Distributions

0. “default”

   defaults only
   

“rrc”

Base Class Documenation: deslib.des.probabilistic.RRC

Param Distributions

0. “default”

   defaults only
   

“single best”

Base Class Documenation: deslib.static.single_best.SingleBest

Param Distributions

0. “default”

   defaults only
   

“stacked”

Base Class Documenation: deslib.static.stacked.StackedClassifier

Param Distributions

0. “default”

   defaults only
   

“stacking classifier”

Base Class Documenation: BPt.pipeline.Ensembles.BPtStackingClassifier

Param Distributions

0. “default”

   defaults only
   

“voting classifier”

Base Class Documenation: BPt.pipeline.Ensembles.BPtVotingClassifier

Param Distributions

0. “voting classifier”

   voting: 'soft'