Leave One Out

class category_encoders.leave_one_out.LeaveOneOutEncoder(verbose=0, cols=None, drop_invariant=False, return_df=True, handle_unknown='value', handle_missing='value', random_state=None, sigma=None)[source]

Leave one out coding for categorical features.

This is very similar to target encoding but excludes the current row’s target when calculating the mean target for a level to reduce the effect of outliers.

Parameters
verbose: int

integer indicating verbosity of the output. 0 for none.

cols: list

a list of columns to encode, if None, all string columns will be encoded.

drop_invariant: bool

boolean for whether or not to drop columns with 0 variance.

return_df: bool

boolean for whether to return a pandas DataFrame from transform (otherwise it will be a numpy array).

handle_missing: str

options are ‘error’, ‘return_nan’ and ‘value’, defaults to ‘value’, which returns the target mean.

handle_unknown: str

options are ‘error’, ‘return_nan’ and ‘value’, defaults to ‘value’, which returns the target mean.

sigma: float

adds normal (Gaussian) distribution noise into training data in order to decrease overfitting (testing data are untouched). Sigma gives the standard deviation (spread or “width”) of the normal distribution. The optimal value is commonly between 0.05 and 0.6. The default is to not add noise, but that leads to significantly suboptimal results.

References

1

Originally by Owen Zhang (reference broken), another short explanation at:

https://datascience.stackexchange.com/questions/10839/what-is-difference-between-one-hot-encoding-and-leave-one-out-encoding

Attributes
feature_names

Methods

fit(X[, y])

Fits the encoder according to X and y.

fit_transform(X[, y])

Encoders that utilize the target must make sure that the training data are transformed with:

get_feature_names()

Returns the names of all transformed / added columns.

get_params([deep])

Get parameters for this estimator.

set_params(**params)

Set the parameters of this estimator.

transform(X[, y, override_return_df])

Perform the transformation to new categorical data.

transform_leave_one_out(X, y[, mapping])

Leave one out encoding uses a single column of floats to represent the means of the target variables.

fit_column_map

fit_leave_one_out

Parameters
verbose: int

integer indicating verbosity of output. 0 for none.

cols: list

a list of columns to encode, if None, all string and categorical columns will be encoded.

drop_invariant: bool

boolean for whether or not to drop columns with 0 variance.

return_df: bool

boolean for whether to return a pandas DataFrame from transform and inverse transform (otherwise it will be a numpy array).

handle_missing: str

how to handle missing values at fit time. Options are ‘error’, ‘return_nan’, and ‘value’. Default ‘value’, which treat NaNs as a countable category at fit time.

handle_unknown: str, int or dict of {columnoption, …}.

how to handle unknown labels at transform time. Options are ‘error’ ‘return_nan’, ‘value’ and int. Defaults to None which uses NaN behaviour specified at fit time. Passing an int will fill with this int value.

kwargs: dict.

additional encoder specific parameters like regularisation.

Attributes
feature_names

Methods

fit(X[, y])

Fits the encoder according to X and y.

fit_transform(X[, y])

Encoders that utilize the target must make sure that the training data are transformed with:

get_feature_names()

Returns the names of all transformed / added columns.

get_params([deep])

Get parameters for this estimator.

set_params(**params)

Set the parameters of this estimator.

transform(X[, y, override_return_df])

Perform the transformation to new categorical data.

transform_leave_one_out(X, y[, mapping])

Leave one out encoding uses a single column of floats to represent the means of the target variables.

fit_column_map

fit_leave_one_out

fit(X, y=None, **kwargs)

Fits the encoder according to X and y.

Parameters
Xarray-like, shape = [n_samples, n_features]

Training vectors, where n_samples is the number of samples and n_features is the number of features.

yarray-like, shape = [n_samples]

Target values.

Returns
selfencoder

Returns self.

fit_transform(X, y=None, **fit_params)
Encoders that utilize the target must make sure that the training data are transformed with:

transform(X, y)

and not with:

transform(X)

get_feature_names() List[str]

Returns the names of all transformed / added columns.

Returns
feature_names: list

A list with all feature names transformed or added. Note: potentially dropped features (because the feature is constant/invariant) are not included!

get_params(deep=True)

Get parameters for this estimator.

Parameters
deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns
paramsdict

Parameter names mapped to their values.

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters
**paramsdict

Estimator parameters.

Returns
selfestimator instance

Estimator instance.

transform(X, y=None, override_return_df=False)

Perform the transformation to new categorical data.

Some encoders behave differently on whether y is given or not. This is mainly due to regularisation in order to avoid overfitting. On training data transform should be called with y, on test data without.

Parameters
Xarray-like, shape = [n_samples, n_features]
yarray-like, shape = [n_samples] or None
override_return_dfbool

override self.return_df to force to return a data frame

Returns
parray or DataFrame, shape = [n_samples, n_features_out]

Transformed values with encoding applied.

transform_leave_one_out(X, y, mapping=None)[source]

Leave one out encoding uses a single column of floats to represent the means of the target variables.