Preprocessing

Scalers, encoders, imputers and discretizers.

BernoulliNB	Naive Bayes classifier for binary features.
Binarizer	Set feature values to 0 or 1 by a threshold.
CategoricalNB	Naive Bayes classifier for categorical features.
chi2	Compute chi-squared statistics between each nonnegative feature and the class labels.
ComplementNB	Complement naive Bayes classifier, suited to imbalanced text data.
f_classif	Compute the ANOVA F-value of each feature against the class labels.
f_regression	Compute the F-statistic of each feature's correlation with a continuous target.
GaussianNB	Gaussian naive Bayes classifier.
IterativeImputer	Impute missing values by modeling each feature from the others, round by round.
KBinsDiscretizer	Bin continuous features into intervals.
LabelBinarizer	Binarize labels in a one-vs-all fashion.
LabelEncoder	Encode target labels as integers 0 to n_classes - 1.
LinearDiscriminantAnalysis	Linear discriminant analysis classifier and supervised dimensionality reduction.
MaxAbsScaler	Scale each feature by its maximum absolute value.
MinMaxScaler	Scale each feature to a given range.
MultiLabelBinarizer	Convert sets of labels to a binary indicator matrix.
MultinomialNB	Naive Bayes classifier for multinomial (count) features.
mutual_info_classif	Estimate the mutual information between each feature and a discrete target.
mutual_info_regression	Estimate the mutual information between each feature and a continuous target.
Normalizer	Scale each row to unit norm.
OneHotEncoder	Encode numeric categorical features as one-hot columns.
OrdinalEncoder	Encode numeric categorical features as integer codes.
PolynomialFeatures	Generate polynomial and interaction features.
PowerTransformer	Apply a power transform per feature to make the data more Gaussian.
preprocessing	Feature scaling: StandardScaler and MinMaxScaler.
QuadraticDiscriminantAnalysis	Quadratic discriminant analysis classifier.
QuantileTransformer	Map each feature to a uniform or normal distribution through its quantiles.
r_regression	Compute the Pearson correlation of each feature with a continuous target.
RFE	Feature ranking with recursive feature elimination.
RobustScaler	Scale features by the median and the interquantile range.
SelectKBest	Select the features with the k highest scores.
SimpleImputer	Replace missing values with a per-column statistic.
SplineTransformer	Generate B-spline basis features for each feature.
StandardScaler	Standardize features by removing the mean and scaling to unit variance.
TargetEncoder	Encode numeric categorical features by the target mean of each category.
VarianceThreshold	Feature selector that removes low-variance features.