dabl.SimpleRegressor

class dabl.SimpleRegressor(refit=True, random_state=None, verbose=1, type_hints=None, shuffle=True)

Automagic anytime classifier.

Parameters

refitboolean, True

Whether to refit the model on the full dataset (I think).

random_staterandom state, int or None (default=None)

Random state or seed.

verboseinteger, default=1

Verbosity (higher is more output).

type_hintsdict or None

If dict, provide type information for columns. Keys are column names, values are types as provided by detect_types.

shuffleboolean, default=True

Whether to shuffle the training set in cross-validation.

__init__(refit=True, random_state=None, verbose=1, type_hints=None, shuffle=True)

Initialize self. See help(type(self)) for accurate signature.

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

Fit regressor.

Requires to either specify the target as separate 1d array or Series y (in scikit-learn fashion) or as column of the dataframe X specified by target_col. If y is specified, X is assumed not to contain the target.

Parameters

XDataFrame

Input features. If target_col is specified, X also includes the target.

ySeries or numpy array, optional.

Target class labels. You need to specify either y or target_col.

target_colstring or int, optional

Column name of target if included in X.

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.

score(X, y, sample_weight=None)

Return the coefficient of determination R^2 of the prediction.

The coefficient R^2 is defined as (1 - \frac{u}{v}), where u is the residual sum of squares ((y_true - y_pred) ** 2).sum() and v is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a R^2 score of 0.0.

Parameters

Xarray-like of shape (n_samples, n_features)

Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.

yarray-like of shape (n_samples,) or (n_samples, n_outputs)

True values for X.

sample_weightarray-like of shape (n_samples,), default=None

Sample weights.

Returns

scorefloat

R^2 of self.predict(X) wrt. y.

Notes

The R^2 score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score(). This influences the score method of all the multioutput regressors (except for MultiOutputRegressor).

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.