TargetEncoder (original) (raw)

class sklearn.preprocessing.TargetEncoder(categories='auto', target_type='auto', smooth='auto', cv=5, shuffle=True, random_state=None)[source]#

Target Encoder for regression and classification targets.

Each category is encoded based on a shrunk estimate of the average target values for observations belonging to the category. The encoding scheme mixes the global target mean with the target mean conditioned on the value of the category (see [MIC]).

When the target type is “multiclass”, encodings are based on the conditional probability estimate for each class. The target is first binarized using the “one-vs-all” scheme viaLabelBinarizer, then the average target value for each class and each category is used for encoding, resulting inn_features * n_classes encoded output features.

TargetEncoder considers missing values, such as np.nan or None, as another category and encodes them like any other category. Categories that are not seen during fit are encoded with the target mean, i.e.target_mean_.

For a demo on the importance of the TargetEncoder internal cross-fitting, seeTarget Encoder’s Internal Cross fitting. For a comparison of different encoders, refer toComparing Target Encoder with Other Encoders. Read more in the User Guide.

Note

fit(X, y).transform(X) does not equal fit_transform(X, y) because across fitting scheme is used in fit_transform for encoding. See the User Guide for details.

Added in version 1.3.

Parameters:

categories“auto” or list of shape (n_features,) of array-like, default=”auto”

Categories (unique values) per feature:

"auto" : Determine categories automatically from the training data.
list : categories[i] holds the categories expected in the i-th column. The passed categories should not mix strings and numeric values within a single feature, and should be sorted in case of numeric values.

The used categories are stored in the categories_ fitted attribute.

target_type{“auto”, “continuous”, “binary”, “multiclass”}, default=”auto”

Type of target.

"auto" : Type of target is inferred withtype_of_target.
"continuous" : Continuous target
"binary" : Binary target
"multiclass" : Multiclass target

Note

The type of target inferred with "auto" may not be the desired target type used for modeling. For example, if the target consisted of integers between 0 and 100, then type_of_targetwill infer the target as "multiclass". In this case, settingtarget_type="continuous" will specify the target as a regression problem. The target_type_ attribute gives the target type used by the encoder.

Changed in version 1.4: Added the option ‘multiclass’.

smooth“auto” or float, default=”auto”

The amount of mixing of the target mean conditioned on the value of the category with the global target mean. A larger smooth value will put more weight on the global target mean. If "auto", then smooth is set to an empirical Bayes estimate.

cvint, default=5

Determines the number of folds in the cross fitting strategy used infit_transform. For classification targets, StratifiedKFold is used and for continuous targets, KFold is used.

shufflebool, default=True

Whether to shuffle the data in fit_transform before splitting into folds. Note that the samples within each split will not be shuffled.

random_stateint, RandomState instance or None, default=None

When shuffle is True, random_state affects the ordering of the indices, which controls the randomness of each fold. Otherwise, this parameter has no effect. Pass an int for reproducible output across multiple function calls. See Glossary.

Attributes:

**encodings_**list of shape (n_features,) or (n_features * n_classes) of ndarray

Encodings learnt on all of X. For feature i, encodings_[i] are the encodings matching the categories listed in categories_[i]. When target_type_ is “multiclass”, the encoding for feature i and class j is stored inencodings_[j + (i * len(classes_))]. E.g., for 2 features (f) and 3 classes (c), encodings are ordered: f0_c0, f0_c1, f0_c2, f1_c0, f1_c1, f1_c2,

**categories_**list of shape (n_features,) of ndarray

The categories of each input feature determined during fitting or specified in categories(in order of the features in X and corresponding with the output of transform).

**target_type_**str

Type of target.

**target_mean_**float

The overall mean of the target. This value is only used in transformto encode categories.

**n_features_in_**int

Number of features seen during fit.

**feature_names_in_**ndarray of shape (n_features_in_,)

Names of features seen during fit. Defined only when Xhas feature names that are all strings.

**classes_**ndarray or None

If target_type_ is ‘binary’ or ‘multiclass’, holds the label for each class, otherwise None.

TargetEncoder (original) (raw)

A high `smooth` parameter puts more weight on global mean on the categorical

encodings:

On the other hand, a low `smooth` parameter puts more weight on target

conditioned on the value of the categorical:

Gallery examples#

TargetEncoder (original) (raw)

A high smooth parameter puts more weight on global mean on the categorical

encodings:

On the other hand, a low smooth parameter puts more weight on target

conditioned on the value of the categorical:

Gallery examples#

A high `smooth` parameter puts more weight on global mean on the categorical

On the other hand, a low `smooth` parameter puts more weight on target