Summary Encoder
- class category_encoders.quantile_encoder.SummaryEncoder(verbose=0, cols=None, drop_invariant=False, return_df=True, handle_missing='value', handle_unknown='value', quantiles=(0.25, 0.75), m=1.0)[source]
Summary Encoding for categorical features.
It’s an encoder designed for creating richer representations by applying quantile encoding for a set of quantiles.
- Parameters
- verbose: int
integer indicating verbosity of the output. 0 for none.
- quantiles: list
list of floats indicating the statistical quantiles. Each element represent a column
- m: float
this is the “m” in the m-probability estimate. Higher value of m results into stronger shrinking. M is non-negative. 0 for no smoothing.
- 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 quantile.
- handle_unknown: str
options are ‘error’, ‘return_nan’ and ‘value’, defaults to ‘value’, which returns the target quantile.
References
- 1
Quantile Encoder: Tackling High Cardinality Categorical Features in Regression Problems, https://link.springer.com/chapter/10.1007%2F978-3-030-85529-1_14
- 2
A Preprocessing Scheme for High-Cardinality Categorical Attributes in Classification and Prediction Problems, equation 7, from https://dl.acm.org/citation.cfm?id=507538
- 3
On estimating probabilities in tree pruning, equation 1, from https://link.springer.com/chapter/10.1007/BFb0017010
- 4
Additive smoothing, from https://en.wikipedia.org/wiki/Additive_smoothing#Generalized_to_the_case_of_known_incidence_rates
- 5
Target encoding done the right way https://maxhalford.github.io/blog/target-encoding/
Methods
fit
(X, y)Fits the encoder according to X and y by fitting the individual encoders.
fit_transform
(X[, y])Encoders that utilize the target must make sure that the training data are transformed with:
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 are not included!.
get_params
([deep])Get parameters for this estimator.
set_params
(**params)Set the parameters of this estimator.
transform
- fit(X, y)[source]
Fits the encoder according to X and y by fitting the individual encoders.
- 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()[source]
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 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.