dabl
.SimpleRegressor¶
-
class
dabl.
SimpleRegressor
(refit=True, random_state=None, verbose=1, type_hints=None, shuffle=True)[source]¶ 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)[source]¶ Initialize self. See help(type(self)) for accurate signature.
-
fit
(X, y=None, *, target_col=None)[source]¶ 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)[source]¶ 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)[source]¶ 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)
, wheren_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 usesmultioutput='uniform_average'
from version 0.23 to keep consistent with default value ofr2_score()
. This influences thescore
method of all the multioutput regressors (except forMultiOutputRegressor
).
-
set_params
(**params)[source]¶ 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.