Available Pipeline Steps

The following is a list of all the available steps that can be used to create a pipeline by name.

Features Preprocessing

Scalers

Name (str)	Description	Class
zscore	Removing mean and scale to unit variance	StandardScaler
scaler_robust	Removing median and scale to IQR	RobustScaler
scaler_minmax	Scale to a given range	MinMaxScaler
scaler_maxabs	Scale by max absolute value	MaxAbsScaler
scaler_normalizer	Normalize to unit norm	Normalizer
scaler_quantile	Transform to uniform or normal distribution (robust)	QuantileTransformer
scaler_power	Gaussianise data	PowerTransformer

Feature Selection

Name (str)	Description	Class
select_univariate	Removing mean and scale to unit variance	GenericUnivariateSelect
select_percentile	Rank and select percentile	SelectPercentile
select_k	Rank and select K	SelectKBest
select_fdr	Select based on estimated FDR	SelectFdr
select_fpr	Select based on FPR threshold	SelectFpr
select_fwe	Select based on FWE threshold	SelectFwe
select_variance	Remove low variance features	VarianceThreshold

Confound Removal

Name (str)	Description	Class
remove_confound	removing confounds from features, by subtracting the prediction of each feature given all confounds. By default this is equal to “independently regressing out the confounds from the features”	confounds.DataFrameConfoundRemover

Decomposition

Name (str)	Description	Class
pca	Principal Component Analysis	PCA

Target Preprocessing

Target Scalers

Name (str)	Description	Class
zscore	Removing mean and scale to unit variance	StandardScaler

Target Confound Removal

Name (str)	Description	Class
remove_confound	removing confounds from target, by subtracting the prediction of the target given all confounds. By default this is equal to “regressing out the confounds from the target”	TargetConfoundRemover

Models

Support Vector Machines

Name (str)	Description	Class	Binary	Multiclass	Regression
svm	Support Vector Machine	SVC and SVR	Y	Y	Y

Ensemble

Name (str)	Description	Class	Binary	Multiclass	Regression
rf	Random Forest	RandomForestClassifier and RandomForestRegressor	Y	Y	Y
et	Extra-Trees	ExtraTreesClassifier and ExtraTreesRegressor	Y	Y	Y
adaboost	AdaBoost	AdaBoostClassifier and AdaBoostRegressor	Y	Y	Y
bagging	Bagging	BaggingClassifier and BaggingRegressor	Y	Y	Y
gradientboost	Gradient Boosting	GradientBoostingClassifier and GradientBoostingRegressor	Y	Y	Y

Gaussian Processes

Name (str)	Description	Class	Binary	Multiclass	Regression
gauss	Gaussian Process	GaussianProcessClassifier and GaussianProcessRegressor	Y	Y	Y

Linear Models

Name (str)	Description	Class	Binary	Multiclass	Regression
logit	Logistic Regression (aka logit, MaxEnt).	LogisticRegression	Y	Y	N
logitcv	Logistic Regression CV (aka logit, MaxEnt).	LogisticRegressionCV	Y	Y	N
linreg	Least Squares regression.	LinearRegression	N	N	Y
ridge	Linear least squares with l2 regularization.	RidgeClassifier and Ridge	Y	Y	Y
ridgecv	Ridge regression with built-in cross-validation.	RidgeClassifierCV and RidgeCV	Y	Y	Y
sgd	Linear model fitted by minimizing a regularized empirical loss with SGD	SGDClassifier and SGDRegressor	Y	Y	Y

Naive Bayes

Name (str)	Description	Class	Binary	Multiclass	Regression
nb_bernoulli	Multivariate Bernoulli models.	BernoulliNB	Y	Y	N
nb_categorical	Categorical features.	CategoricalNB	Y	Y	N
nb_complement	Complement Naive Bayes	ComplementNB	Y	Y	N
nb_gaussian	Gaussian Naive Bayes	GaussianNB	Y	Y	N
nb_multinomial	Multinomial models	MultinomialNB	Y	Y	N

Dummy

Name (str)	Description	Class	Binary	Multiclass	Regression
dummy	Use simple rules (without features).	DummyClassifier and DummyRegressor	Y	Y	Y