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Models

Different base obj choices for the :class:`Model<BPt.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: :class:`sklearn.tree.DecisionTreeClassifier`

Param Distributions

  1. "default"

    defaults only

  2. "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: :class:`sklearn.linear_model.LogisticRegression`

Param Distributions

  1. "base elastic"

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

  2. "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)

  3. "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)

  4. "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: :class:`sklearn.ensemble.ExtraTreesClassifier`

Param Distributions

  1. "default"

    defaults only

"gaussian nb"

Base Class Documenation: :class:`sklearn.naive_bayes.GaussianNB`

Param Distributions

  1. "base gnb"

    var_smoothing: 1e-9

"gb classifier"

Base Class Documenation: :class:`sklearn.ensemble.GradientBoostingClassifier`

Param Distributions

  1. "default"

    defaults only

"gp classifier"

Base Class Documenation: :class:`sklearn.gaussian_process.GaussianProcessClassifier`

Param Distributions

  1. "base gp classifier"

    n_restarts_optimizer: 5

"hgb classifier"

Base Class Documenation: :class:`sklearn.ensemble.gradient_boosting.HistGradientBoostingClassifier`

Param Distributions

  1. "default"

    defaults only

"knn classifier"

Base Class Documenation: :class:`sklearn.neighbors.KNeighborsClassifier`

Param Distributions

  1. "base knn"

    n_neighbors: 5

  2. "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: :class:`sklearn.linear_model.LogisticRegression`

Param Distributions

  1. "base lasso"

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

  2. "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)

  3. "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: :class:`lightgbm.LGBMClassifier`

Param Distributions

  1. "base lgbm"

    silent: True

  2. "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'])

  3. "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: :class:`sklearn.svm.LinearSVC`

Param Distributions

  1. "base linear svc"

    max_iter: 1000

  2. "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: :class:`sklearn.linear_model.LogisticRegression`

Param Distributions

  1. "base logistic"

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

"mlp classifier"

Base Class Documenation: :class:`BPt.extensions.MLP.MLPClassifier_Wrapper`

Param Distributions

  1. "default"

    defaults only

  2. "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)

  3. "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)

  4. "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)

  5. "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)

  6. "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)

  7. "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: :class:`sklearn.linear_model.PassiveAggressiveClassifier`

Param Distributions

  1. "default"

    defaults only

"random forest classifier"

Base Class Documenation: :class:`sklearn.ensemble.RandomForestClassifier`

Param Distributions

  1. "base rf regressor"

    n_estimators: 100

  2. "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: :class:`sklearn.linear_model.LogisticRegression`

Param Distributions

  1. "base ridge"

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

  2. "ridge C"

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

  3. "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: :class:`sklearn.linear_model.SGDClassifier`

Param Distributions

  1. "base sgd"

    loss: 'hinge'

  2. "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: :class:`sklearn.svm.SVC`

Param Distributions

  1. "base svm classifier"

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

  2. "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: :class:`xgboost.XGBClassifier`

Param Distributions

  1. "base xgb classifier"

    verbosity: 0
objective: 'binary:logistic'

  2. "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)])

  3. "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)

  4. "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: :class:`sklearn.linear_model.ARDRegression`

Param Distributions

  1. "default"

    defaults only

"bayesian ridge regressor"

Base Class Documenation: :class:`sklearn.linear_model.BayesianRidge`

Param Distributions

  1. "default"

    defaults only

"dt regressor"

Base Class Documenation: :class:`sklearn.tree.DecisionTreeRegressor`

Param Distributions

  1. "default"

    defaults only

  2. "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: :class:`sklearn.linear_model.ElasticNet`

Param Distributions

  1. "base elastic net"

    max_iter: 1000

  2. "elastic regression"

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

  3. "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: :class:`sklearn.ensemble.ExtraTreesRegressor`

Param Distributions

  1. "default"

    defaults only

"gb regressor"

Base Class Documenation: :class:`sklearn.ensemble.GradientBoostingRegressor`

Param Distributions

  1. "default"

    defaults only

"gp regressor"

Base Class Documenation: :class:`sklearn.gaussian_process.GaussianProcessRegressor`

Param Distributions

  1. "base gp regressor"

    n_restarts_optimizer: 5
normalize_y: True

"hgb regressor"

Base Class Documenation: :class:`sklearn.ensemble.gradient_boosting.HistGradientBoostingRegressor`

Param Distributions

  1. "default"

    defaults only

"knn regressor"

Base Class Documenation: :class:`sklearn.neighbors.KNeighborsRegressor`

Param Distributions

  1. "base knn regression"

    n_neighbors: 5

  2. "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: :class:`sklearn.linear_model.Lasso`

Param Distributions

  1. "base lasso regressor"

    max_iter: 1000

  2. "lasso regressor dist"

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

"light gbm regressor"

Base Class Documenation: :class:`lightgbm.LGBMRegressor`

Param Distributions

  1. "base lgbm"

    silent: True

  2. "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)])

  3. "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: :class:`sklearn.linear_model.LinearRegression`

Param Distributions

  1. "base linear"

    fit_intercept: True

"linear svm regressor"

Base Class Documenation: :class:`sklearn.svm.LinearSVR`

Param Distributions

  1. "base linear svr"

    loss: 'epsilon_insensitive'
max_iter: 1000

  2. "linear svr dist"

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

"mlp regressor"

Base Class Documenation: :class:`BPt.extensions.MLP.MLPRegressor_Wrapper`

Param Distributions

  1. "default"

    defaults only

  2. "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)

  3. "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)

  4. "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)

  5. "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)

  6. "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)

  7. "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: :class:`sklearn.ensemble.RandomForestRegressor`

Param Distributions

  1. "base rf"

    n_estimators: 100

  2. "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: :class:`sklearn.linear_model.Ridge`

Param Distributions

  1. "base ridge regressor"

    max_iter: 1000
solver: 'lsqr'

  2. "ridge regressor dist"

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

"svm regressor"

Base Class Documenation: :class:`sklearn.svm.SVR`

Param Distributions

  1. "base svm"

    kernel: 'rbf'
gamma: 'scale'

  2. "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: :class:`sklearn.linear_model.glm.TweedieRegressor`

Param Distributions

  1. "default"

    defaults only

"xgb regressor"

Base Class Documenation: :class:`xgboost.XGBRegressor`

Param Distributions

  1. "base xgb"

    verbosity: 0
objective: 'reg:squarederror'

  2. "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)])

  3. "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)

  4. "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: :class:`sklearn.tree.DecisionTreeClassifier`

Param Distributions

  1. "default"

    defaults only

  2. "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: :class:`sklearn.linear_model.LogisticRegression`

Param Distributions

  1. "base elastic"

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

  2. "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)

  3. "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)

  4. "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: :class:`sklearn.ensemble.ExtraTreesClassifier`

Param Distributions

  1. "default"

    defaults only

"gaussian nb"

Base Class Documenation: :class:`sklearn.naive_bayes.GaussianNB`

Param Distributions

  1. "base gnb"

    var_smoothing: 1e-9

"gb classifier"

Base Class Documenation: :class:`sklearn.ensemble.GradientBoostingClassifier`

Param Distributions

  1. "default"

    defaults only

"gp classifier"

Base Class Documenation: :class:`sklearn.gaussian_process.GaussianProcessClassifier`

Param Distributions

  1. "base gp classifier"

    n_restarts_optimizer: 5

"hgb classifier"

Base Class Documenation: :class:`sklearn.ensemble.gradient_boosting.HistGradientBoostingClassifier`

Param Distributions

  1. "default"

    defaults only

"knn classifier"

Base Class Documenation: :class:`sklearn.neighbors.KNeighborsClassifier`

Param Distributions

  1. "base knn"

    n_neighbors: 5

  2. "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: :class:`sklearn.linear_model.LogisticRegression`

Param Distributions

  1. "base lasso"

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

  2. "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)

  3. "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: :class:`lightgbm.LGBMClassifier`

Param Distributions

  1. "base lgbm"

    silent: True

  2. "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'])

  3. "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: :class:`sklearn.svm.LinearSVC`

Param Distributions

  1. "base linear svc"

    max_iter: 1000

  2. "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: :class:`sklearn.linear_model.LogisticRegression`

Param Distributions

  1. "base logistic"

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

"mlp classifier"

Base Class Documenation: :class:`BPt.extensions.MLP.MLPClassifier_Wrapper`

Param Distributions

  1. "default"

    defaults only

  2. "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)

  3. "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)

  4. "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)

  5. "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)

  6. "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)

  7. "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: :class:`sklearn.linear_model.PassiveAggressiveClassifier`

Param Distributions

  1. "default"

    defaults only

"random forest classifier"

Base Class Documenation: :class:`sklearn.ensemble.RandomForestClassifier`

Param Distributions

  1. "base rf regressor"

    n_estimators: 100

  2. "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: :class:`sklearn.linear_model.LogisticRegression`

Param Distributions

  1. "base ridge"

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

  2. "ridge C"

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

  3. "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: :class:`sklearn.linear_model.SGDClassifier`

Param Distributions

  1. "base sgd"

    loss: 'hinge'

  2. "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: :class:`sklearn.svm.SVC`

Param Distributions

  1. "base svm classifier"

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

  2. "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: :class:`xgboost.XGBClassifier`

Param Distributions

  1. "base xgb classifier"

    verbosity: 0
objective: 'binary:logistic'

  2. "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)])

  3. "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)

  4. "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 :class:`Problem_Spec<BPt.Problem_Spec>`, :class:`Param_Search<BPt.Param_Search>` and :class:`Feat_Importance<BPt.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: :func:`sklearn.metrics.accuracy_score`

"roc_auc"

Base Func Documenation: :func:`sklearn.metrics.roc_auc_score`

"roc_auc_ovr"

Base Func Documenation: :func:`sklearn.metrics.roc_auc_score`

"roc_auc_ovo"

Base Func Documenation: :func:`sklearn.metrics.roc_auc_score`

"roc_auc_ovr_weighted"

Base Func Documenation: :func:`sklearn.metrics.roc_auc_score`

"roc_auc_ovo_weighted"

Base Func Documenation: :func:`sklearn.metrics.roc_auc_score`

"balanced_accuracy"

Base Func Documenation: :func:`sklearn.metrics.balanced_accuracy_score`

"average_precision"

Base Func Documenation: :func:`sklearn.metrics.average_precision_score`

"neg_log_loss"

Base Func Documenation: :func:`sklearn.metrics.log_loss`

"neg_brier_score"

Base Func Documenation: :func:`sklearn.metrics.brier_score_loss`

"precision"

Base Func Documenation: :func:`sklearn.metrics.precision_score`

"precision_macro"

Base Func Documenation: :func:`sklearn.metrics.precision_score`

"precision_micro"

Base Func Documenation: :func:`sklearn.metrics.precision_score`

"precision_samples"

Base Func Documenation: :func:`sklearn.metrics.precision_score`

"precision_weighted"

Base Func Documenation: :func:`sklearn.metrics.precision_score`

"recall"

Base Func Documenation: :func:`sklearn.metrics.recall_score`

"recall_macro"

Base Func Documenation: :func:`sklearn.metrics.recall_score`

"recall_micro"

Base Func Documenation: :func:`sklearn.metrics.recall_score`

"recall_samples"

Base Func Documenation: :func:`sklearn.metrics.recall_score`

"recall_weighted"

Base Func Documenation: :func:`sklearn.metrics.recall_score`

"f1"

Base Func Documenation: :func:`sklearn.metrics.f1_score`

"f1_macro"

Base Func Documenation: :func:`sklearn.metrics.f1_score`

"f1_micro"

Base Func Documenation: :func:`sklearn.metrics.f1_score`

"f1_samples"

Base Func Documenation: :func:`sklearn.metrics.f1_score`

"f1_weighted"

Base Func Documenation: :func:`sklearn.metrics.f1_score`

"jaccard"

Base Func Documenation: :func:`sklearn.metrics.jaccard_score`

"jaccard_macro"

Base Func Documenation: :func:`sklearn.metrics.jaccard_score`

"jaccard_micro"

Base Func Documenation: :func:`sklearn.metrics.jaccard_score`

"jaccard_samples"

Base Func Documenation: :func:`sklearn.metrics.jaccard_score`

"jaccard_weighted"

Base Func Documenation: :func:`sklearn.metrics.jaccard_score`

"neg_hamming"

Base Func Documenation: :func:`sklearn.metrics.hamming_loss`

"matthews"

Base Func Documenation: :func:`sklearn.metrics.matthews_corrcoef`

"default"

Base Func Documenation: :func:`sklearn.metrics.roc_auc_score`

regression

"explained_variance"

Base Func Documenation: :func:`sklearn.metrics.explained_variance_score`

"explained_variance score"

Base Func Documenation: :func:`sklearn.metrics.explained_variance_score`

"r2"

Base Func Documenation: :func:`sklearn.metrics.r2_score`

"max_error"

Base Func Documenation: :func:`sklearn.metrics.max_error`

"neg_median_absolute_error"

Base Func Documenation: :func:`sklearn.metrics.median_absolute_error`

"median_absolute_error"

Base Func Documenation: :func:`sklearn.metrics.median_absolute_error`

"neg_mean_absolute_error"

Base Func Documenation: :func:`sklearn.metrics.mean_absolute_error`

"mean_absolute_error"

Base Func Documenation: :func:`sklearn.metrics.mean_absolute_error`

"neg_mean_squared_error"

Base Func Documenation: :func:`sklearn.metrics.mean_squared_error`

"mean_squared_error"

Base Func Documenation: :func:`sklearn.metrics.mean_squared_error`

"neg_mean_squared_log_error"

Base Func Documenation: :func:`sklearn.metrics.mean_squared_log_error`

"mean_squared_log_error"

Base Func Documenation: :func:`sklearn.metrics.mean_squared_log_error`

"neg_root_mean_squared_error"

Base Func Documenation: :func:`sklearn.metrics.mean_squared_error`

"root_mean_squared_error"

Base Func Documenation: :func:`sklearn.metrics.mean_squared_error`

"neg_mean_poisson_deviance"

Base Func Documenation: :func:`sklearn.metrics.mean_poisson_deviance`

"mean_poisson_deviance"

Base Func Documenation: :func:`sklearn.metrics.mean_poisson_deviance`

"neg_mean_gamma_deviance"

Base Func Documenation: :func:`sklearn.metrics.mean_gamma_deviance`

"mean_gamma_deviance"

Base Func Documenation: :func:`sklearn.metrics.mean_gamma_deviance`

"default"

Base Func Documenation: :func:`sklearn.metrics.r2_score`

categorical

"accuracy"

Base Func Documenation: :func:`sklearn.metrics.accuracy_score`

"roc_auc"

Base Func Documenation: :func:`sklearn.metrics.roc_auc_score`

"roc_auc_ovr"

Base Func Documenation: :func:`sklearn.metrics.roc_auc_score`

"roc_auc_ovo"

Base Func Documenation: :func:`sklearn.metrics.roc_auc_score`

"roc_auc_ovr_weighted"

Base Func Documenation: :func:`sklearn.metrics.roc_auc_score`

"roc_auc_ovo_weighted"

Base Func Documenation: :func:`sklearn.metrics.roc_auc_score`

"balanced_accuracy"

Base Func Documenation: :func:`sklearn.metrics.balanced_accuracy_score`

"average_precision"

Base Func Documenation: :func:`sklearn.metrics.average_precision_score`

"neg_log_loss"

Base Func Documenation: :func:`sklearn.metrics.log_loss`

"neg_brier_score"

Base Func Documenation: :func:`sklearn.metrics.brier_score_loss`

"precision"

Base Func Documenation: :func:`sklearn.metrics.precision_score`

"precision_macro"

Base Func Documenation: :func:`sklearn.metrics.precision_score`

"precision_micro"

Base Func Documenation: :func:`sklearn.metrics.precision_score`

"precision_samples"

Base Func Documenation: :func:`sklearn.metrics.precision_score`

"precision_weighted"

Base Func Documenation: :func:`sklearn.metrics.precision_score`

"recall"

Base Func Documenation: :func:`sklearn.metrics.recall_score`

"recall_macro"

Base Func Documenation: :func:`sklearn.metrics.recall_score`

"recall_micro"

Base Func Documenation: :func:`sklearn.metrics.recall_score`

"recall_samples"

Base Func Documenation: :func:`sklearn.metrics.recall_score`

"recall_weighted"

Base Func Documenation: :func:`sklearn.metrics.recall_score`

"f1"

Base Func Documenation: :func:`sklearn.metrics.f1_score`

"f1_macro"

Base Func Documenation: :func:`sklearn.metrics.f1_score`

"f1_micro"

Base Func Documenation: :func:`sklearn.metrics.f1_score`

"f1_samples"

Base Func Documenation: :func:`sklearn.metrics.f1_score`

"f1_weighted"

Base Func Documenation: :func:`sklearn.metrics.f1_score`

"jaccard"

Base Func Documenation: :func:`sklearn.metrics.jaccard_score`

"jaccard_macro"

Base Func Documenation: :func:`sklearn.metrics.jaccard_score`

"jaccard_micro"

Base Func Documenation: :func:`sklearn.metrics.jaccard_score`

"jaccard_samples"

Base Func Documenation: :func:`sklearn.metrics.jaccard_score`

"jaccard_weighted"

Base Func Documenation: :func:`sklearn.metrics.jaccard_score`

"neg_hamming"

Base Func Documenation: :func:`sklearn.metrics.hamming_loss`

"matthews"

Base Func Documenation: :func:`sklearn.metrics.matthews_corrcoef`

"default"

Base Func Documenation: :func:`sklearn.metrics.roc_auc_score`

Loaders

Different base obj choices for the :class:`Loader<BPt.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: :class:`BPt.extensions.Loaders.Identity`

Param Distributions

  1. "default"

    defaults only

"surface rois"

Base Class Documenation: :class:`BPt.extensions.Loaders.SurfLabels`

Param Distributions

  1. "default"

    defaults only

"volume rois"

Base Class Documenation: :class:`nilearn.input_data.nifti_labels_masker.NiftiLabelsMasker`

Param Distributions

  1. "default"

    defaults only

"connectivity"

Base Class Documenation: :class:`BPt.extensions.Loaders.Connectivity`

Param Distributions

  1. "default"

    defaults only

Imputers

Different base obj choices for the :class:`Imputer<BPt.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: :class:`sklearn.impute.SimpleImputer`

Param Distributions

  1. "mean imp"

    strategy: 'mean'

"median"

Base Class Documenation: :class:`sklearn.impute.SimpleImputer`

Param Distributions

  1. "median imp"

    strategy: 'median'

"most frequent"

Base Class Documenation: :class:`sklearn.impute.SimpleImputer`

Param Distributions

  1. "most freq imp"

    strategy: 'most_frequent'

"constant"

Base Class Documenation: :class:`sklearn.impute.SimpleImputer`

Param Distributions

  1. "constant imp"

    strategy: 'constant'

"iterative"

Base Class Documenation: :class:`sklearn.impute.IterativeImputer`

Param Distributions

  1. "iterative imp"

    initial_strategy: 'mean'
skip_complete: True

Scalers

Different base obj choices for the :class:`Scaler<BPt.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: :class:`sklearn.preprocessing.StandardScaler`

Param Distributions

  1. "base standard"

    with_mean: True
with_std: True

"minmax"

Base Class Documenation: :class:`sklearn.preprocessing.MinMaxScaler`

Param Distributions

  1. "base minmax"

    feature_range: (0, 1)

"maxabs"

Base Class Documenation: :class:`sklearn.preprocessing.MaxAbsScaler`

Param Distributions

  1. "default"

    defaults only

"robust"

Base Class Documenation: :class:`sklearn.preprocessing.RobustScaler`

Param Distributions

  1. "base robust"

    quantile_range: (5, 95)

  2. "robust gs"

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

"yeo"

Base Class Documenation: :class:`sklearn.preprocessing.PowerTransformer`

Param Distributions

  1. "base yeo"

    method: 'yeo-johnson'
standardize: True

"boxcox"

Base Class Documenation: :class:`sklearn.preprocessing.PowerTransformer`

Param Distributions

  1. "base boxcox"

    method: 'box-cox'
standardize: True

"winsorize"

Base Class Documenation: :class:`BPt.extensions.Scalers.Winsorizer`

Param Distributions

  1. "base winsorize"

    quantile_range: (1, 99)

  2. "winsorize gs"

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

"quantile norm"

Base Class Documenation: :class:`sklearn.preprocessing.QuantileTransformer`

Param Distributions

  1. "base quant norm"

    output_distribution: 'normal'

"quantile uniform"

Base Class Documenation: :class:`sklearn.preprocessing.QuantileTransformer`

Param Distributions

  1. "base quant uniform"

    output_distribution: 'uniform'

"normalize"

Base Class Documenation: :class:`sklearn.preprocessing.Normalizer`

Param Distributions

  1. "default"

    defaults only

Transformers

Different base obj choices for the :class:`Transformer<BPt.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: :class:`sklearn.decomposition.PCA`

Param Distributions

  1. "default"

    defaults only

  2. "pca var search"

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

"sparse pca"

Base Class Documenation: :class:`sklearn.decomposition.SparsePCA`

Param Distributions

  1. "default"

    defaults only

"mini batch sparse pca"

Base Class Documenation: :class:`sklearn.decomposition.MiniBatchSparsePCA`

Param Distributions

  1. "default"

    defaults only

"factor analysis"

Base Class Documenation: :class:`sklearn.decomposition.FactorAnalysis`

Param Distributions

  1. "default"

    defaults only

"dictionary learning"

Base Class Documenation: :class:`sklearn.decomposition.DictionaryLearning`

Param Distributions

  1. "default"

    defaults only

"mini batch dictionary learning"

Base Class Documenation: :class:`sklearn.decomposition.MiniBatchDictionaryLearning`

Param Distributions

  1. "default"

    defaults only

"fast ica"

Base Class Documenation: :class:`sklearn.decomposition.FastICA`

Param Distributions

  1. "default"

    defaults only

"incremental pca"

Base Class Documenation: :class:`sklearn.decomposition.IncrementalPCA`

Param Distributions

  1. "default"

    defaults only

"kernel pca"

Base Class Documenation: :class:`sklearn.decomposition.KernelPCA`

Param Distributions

  1. "default"

    defaults only

"nmf"

Base Class Documenation: :class:`sklearn.decomposition.NMF`

Param Distributions

  1. "default"

    defaults only

"truncated svd"

Base Class Documenation: :class:`sklearn.decomposition.TruncatedSVD`

Param Distributions

  1. "default"

    defaults only

"one hot encoder"

Base Class Documenation: :class:`sklearn.preprocessing.OneHotEncoder`

Param Distributions

  1. "ohe"

    sparse: False
handle_unknown: 'ignore'

"backward difference encoder"

Base Class Documenation: :class:`category_encoders.backward_difference.BackwardDifferenceEncoder`

Param Distributions

  1. "default"

    defaults only

"binary encoder"

Base Class Documenation: :class:`category_encoders.binary.BinaryEncoder`

Param Distributions

  1. "default"

    defaults only

"cat boost encoder"

Base Class Documenation: :class:`category_encoders.cat_boost.CatBoostEncoder`

Param Distributions

  1. "default"

    defaults only

"helmert encoder"

Base Class Documenation: :class:`category_encoders.helmert.HelmertEncoder`

Param Distributions

  1. "default"

    defaults only

"james stein encoder"

Base Class Documenation: :class:`category_encoders.james_stein.JamesSteinEncoder`

Param Distributions

  1. "default"

    defaults only

"leave one out encoder"

Base Class Documenation: :class:`category_encoders.leave_one_out.LeaveOneOutEncoder`

Param Distributions

  1. "default"

    defaults only

"m estimate encoder"

Base Class Documenation: :class:`category_encoders.m_estimate.MEstimateEncoder`

Param Distributions

  1. "default"

    defaults only

"polynomial encoder"

Base Class Documenation: :class:`category_encoders.polynomial.PolynomialEncoder`

Param Distributions

  1. "default"

    defaults only

"sum encoder"

Base Class Documenation: :class:`category_encoders.sum_coding.SumEncoder`

Param Distributions

  1. "default"

    defaults only

"target encoder"

Base Class Documenation: :class:`category_encoders.target_encoder.TargetEncoder`

Param Distributions

  1. "default"

    defaults only

"woe encoder"

Base Class Documenation: :class:`category_encoders.woe.WOEEncoder`

Param Distributions

  1. "default"

    defaults only

Feat Selectors

Different base obj choices for the :class:`Feat_Selector<BPt.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: :class:`sklearn.feature_selection.RFE`

Param Distributions

  1. "base rfe"

    n_features_to_select: None

  2. "rfe num feats dist"

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

"selector"

Base Class Documenation: :class:`BPt.extensions.Feat_Selectors.FeatureSelector`

Param Distributions

  1. "random"

    mask: 'sets as random features'

  2. "searchable"

    mask: 'sets as hyperparameters'

"univariate selection c"

Base Class Documenation: :class:`sklearn.feature_selection.SelectPercentile`

Param Distributions

  1. "base univar fs classifier"

    score_func: f_classif
percentile: 50

  2. "univar fs classifier dist"

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

  3. "univar fs classifier dist2"

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

"variance threshold"

Base Class Documenation: :class:`sklearn.feature_selection.VarianceThreshold`

Param Distributions

  1. "default"

    defaults only

regression

"rfe"

Base Class Documenation: :class:`sklearn.feature_selection.RFE`

Param Distributions

  1. "base rfe"

    n_features_to_select: None

  2. "rfe num feats dist"

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

"selector"

Base Class Documenation: :class:`BPt.extensions.Feat_Selectors.FeatureSelector`

Param Distributions

  1. "random"

    mask: 'sets as random features'

  2. "searchable"

    mask: 'sets as hyperparameters'

"univariate selection r"

Base Class Documenation: :class:`sklearn.feature_selection.SelectPercentile`

Param Distributions

  1. "base univar fs regression"

    score_func: f_regression
percentile: 50

  2. "univar fs regression dist"

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

  3. "univar fs regression dist2"

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

"variance threshold"

Base Class Documenation: :class:`sklearn.feature_selection.VarianceThreshold`

Param Distributions

  1. "default"

    defaults only

categorical

"rfe"

Base Class Documenation: :class:`sklearn.feature_selection.RFE`

Param Distributions

  1. "base rfe"

    n_features_to_select: None

  2. "rfe num feats dist"

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

"selector"

Base Class Documenation: :class:`BPt.extensions.Feat_Selectors.FeatureSelector`

Param Distributions

  1. "random"

    mask: 'sets as random features'

  2. "searchable"

    mask: 'sets as hyperparameters'

"univariate selection c"

Base Class Documenation: :class:`sklearn.feature_selection.SelectPercentile`

Param Distributions

  1. "base univar fs classifier"

    score_func: f_classif
percentile: 50

  2. "univar fs classifier dist"

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

  3. "univar fs classifier dist2"

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

"variance threshold"

Base Class Documenation: :class:`sklearn.feature_selection.VarianceThreshold`

Param Distributions

  1. "default"

    defaults only

Ensemble Types

Different base obj choices for the :class:`Ensemble<BPt.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: :class:`sklearn.ensemble.AdaBoostClassifier`

Param Distributions

  1. "default"

    defaults only

"aposteriori"

Base Class Documenation: :class:`deslib.dcs.a_posteriori.APosteriori`

Param Distributions

  1. "default"

    defaults only

"apriori"

Base Class Documenation: :class:`deslib.dcs.a_priori.APriori`

Param Distributions

  1. "default"

    defaults only

"bagging classifier"

Base Class Documenation: :class:`sklearn.ensemble.BaggingClassifier`

Param Distributions

  1. "default"

    defaults only

"balanced bagging classifier"

Base Class Documenation: :class:`imblearn.ensemble.BalancedBaggingClassifier`

Param Distributions

  1. "default"

    defaults only

"des clustering"

Base Class Documenation: :class:`deslib.des.des_clustering.DESClustering`

Param Distributions

  1. "default"

    defaults only

"des knn"

Base Class Documenation: :class:`deslib.des.des_knn.DESKNN`

Param Distributions

  1. "default"

    defaults only

"deskl"

Base Class Documenation: :class:`deslib.des.probabilistic.DESKL`

Param Distributions

  1. "default"

    defaults only

"desmi"

Base Class Documenation: :class:`deslib.des.des_mi.DESMI`

Param Distributions

  1. "default"

    defaults only

"desp"

Base Class Documenation: :class:`deslib.des.des_p.DESP`

Param Distributions

  1. "default"

    defaults only

"exponential"

Base Class Documenation: :class:`deslib.des.probabilistic.Exponential`

Param Distributions

  1. "default"

    defaults only

"knop"

Base Class Documenation: :class:`deslib.des.knop.KNOP`

Param Distributions

  1. "default"

    defaults only

"knorae"

Base Class Documenation: :class:`deslib.des.knora_e.KNORAE`

Param Distributions

  1. "default"

    defaults only

"knrau"

Base Class Documenation: :class:`deslib.des.knora_u.KNORAU`

Param Distributions

  1. "default"

    defaults only

"lca"

Base Class Documenation: :class:`deslib.dcs.lca.LCA`

Param Distributions

  1. "default"

    defaults only

"logarithmic"

Base Class Documenation: :class:`deslib.des.probabilistic.Logarithmic`

Param Distributions

  1. "default"

    defaults only

"mcb"

Base Class Documenation: :class:`deslib.dcs.mcb.MCB`

Param Distributions

  1. "default"

    defaults only

"metades"

Base Class Documenation: :class:`deslib.des.meta_des.METADES`

Param Distributions

  1. "default"

    defaults only

"min dif"

Base Class Documenation: :class:`deslib.des.probabilistic.MinimumDifference`

Param Distributions

  1. "default"

    defaults only

"mla"

Base Class Documenation: :class:`deslib.dcs.mla.MLA`

Param Distributions

  1. "default"

    defaults only

"ola"

Base Class Documenation: :class:`deslib.dcs.ola.OLA`

Param Distributions

  1. "default"

    defaults only

"rank"

Base Class Documenation: :class:`deslib.dcs.rank.Rank`

Param Distributions

  1. "default"

    defaults only

"rrc"

Base Class Documenation: :class:`deslib.des.probabilistic.RRC`

Param Distributions

  1. "default"

    defaults only

"single best"

Base Class Documenation: :class:`deslib.static.single_best.SingleBest`

Param Distributions

  1. "default"

    defaults only

"stacked"

Base Class Documenation: :class:`deslib.static.stacked.StackedClassifier`

Param Distributions

  1. "default"

    defaults only

"stacking classifier"

Base Class Documenation: :class:`BPt.pipeline.Ensembles.BPtStackingClassifier`

Param Distributions

  1. "default"

    defaults only

"voting classifier"

Base Class Documenation: :class:`BPt.pipeline.Ensembles.BPtVotingClassifier`

Param Distributions

  1. "voting classifier"

    voting: 'soft'

regression

"adaboost regressor"

Base Class Documenation: :class:`sklearn.ensemble.AdaBoostRegressor`

Param Distributions

  1. "default"

    defaults only

"bagging regressor"

Base Class Documenation: :class:`sklearn.ensemble.BaggingRegressor`

Param Distributions

  1. "default"

    defaults only

"stacking regressor"

Base Class Documenation: :class:`BPt.pipeline.Ensembles.BPtStackingRegressor`

Param Distributions

  1. "default"

    defaults only

"voting regressor"

Base Class Documenation: :class:`BPt.pipeline.Ensembles.BPtVotingRegressor`

Param Distributions

  1. "default"

    defaults only

categorical

"adaboost classifier"

Base Class Documenation: :class:`sklearn.ensemble.AdaBoostClassifier`

Param Distributions

  1. "default"

    defaults only

"aposteriori"

Base Class Documenation: :class:`deslib.dcs.a_posteriori.APosteriori`

Param Distributions

  1. "default"

    defaults only

"apriori"

Base Class Documenation: :class:`deslib.dcs.a_priori.APriori`

Param Distributions

  1. "default"

    defaults only

"bagging classifier"

Base Class Documenation: :class:`sklearn.ensemble.BaggingClassifier`

Param Distributions

  1. "default"

    defaults only

"balanced bagging classifier"

Base Class Documenation: :class:`imblearn.ensemble.BalancedBaggingClassifier`

Param Distributions

  1. "default"

    defaults only

"des clustering"

Base Class Documenation: :class:`deslib.des.des_clustering.DESClustering`

Param Distributions

  1. "default"

    defaults only

"des knn"

Base Class Documenation: :class:`deslib.des.des_knn.DESKNN`

Param Distributions

  1. "default"

    defaults only

"deskl"

Base Class Documenation: :class:`deslib.des.probabilistic.DESKL`

Param Distributions

  1. "default"

    defaults only

"desmi"

Base Class Documenation: :class:`deslib.des.des_mi.DESMI`

Param Distributions

  1. "default"

    defaults only

"desp"

Base Class Documenation: :class:`deslib.des.des_p.DESP`

Param Distributions

  1. "default"

    defaults only

"exponential"

Base Class Documenation: :class:`deslib.des.probabilistic.Exponential`

Param Distributions

  1. "default"

    defaults only

"knop"

Base Class Documenation: :class:`deslib.des.knop.KNOP`

Param Distributions

  1. "default"

    defaults only

"knorae"

Base Class Documenation: :class:`deslib.des.knora_e.KNORAE`

Param Distributions

  1. "default"

    defaults only

"knrau"

Base Class Documenation: :class:`deslib.des.knora_u.KNORAU`

Param Distributions

  1. "default"

    defaults only

"lca"

Base Class Documenation: :class:`deslib.dcs.lca.LCA`

Param Distributions

  1. "default"

    defaults only

"logarithmic"

Base Class Documenation: :class:`deslib.des.probabilistic.Logarithmic`

Param Distributions

  1. "default"

    defaults only

"mcb"

Base Class Documenation: :class:`deslib.dcs.mcb.MCB`

Param Distributions

  1. "default"

    defaults only

"metades"

Base Class Documenation: :class:`deslib.des.meta_des.METADES`

Param Distributions

  1. "default"

    defaults only

"min dif"

Base Class Documenation: :class:`deslib.des.probabilistic.MinimumDifference`

Param Distributions

  1. "default"

    defaults only

"mla"

Base Class Documenation: :class:`deslib.dcs.mla.MLA`

Param Distributions

  1. "default"

    defaults only

"ola"

Base Class Documenation: :class:`deslib.dcs.ola.OLA`

Param Distributions

  1. "default"

    defaults only

"rank"

Base Class Documenation: :class:`deslib.dcs.rank.Rank`

Param Distributions

  1. "default"

    defaults only

"rrc"

Base Class Documenation: :class:`deslib.des.probabilistic.RRC`

Param Distributions

  1. "default"

    defaults only

"single best"

Base Class Documenation: :class:`deslib.static.single_best.SingleBest`

Param Distributions

  1. "default"

    defaults only

"stacked"

Base Class Documenation: :class:`deslib.static.stacked.StackedClassifier`

Param Distributions

  1. "default"

    defaults only

"stacking classifier"

Base Class Documenation: :class:`BPt.pipeline.Ensembles.BPtStackingClassifier`

Param Distributions

  1. "default"

    defaults only

"voting classifier"

Base Class Documenation: :class:`BPt.pipeline.Ensembles.BPtVotingClassifier`

Param Distributions

  1. "voting classifier"

    voting: 'soft'