dabl
.SimpleClassifier¶
-
class
dabl.
SimpleClassifier
(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.
- 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.
- Attributes
- est_sklearn estimator
Best estimator found.
-
__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 classifier.
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 mean accuracy on the given test data and labels.
In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.
- Parameters
- Xarray-like of shape (n_samples, n_features)
Test samples.
- yarray-like of shape (n_samples,) or (n_samples, n_outputs)
True labels for X.
- sample_weightarray-like of shape (n_samples,), default=None
Sample weights.
- Returns
- scorefloat
Mean accuracy of
self.predict(X)
wrt. y.
-
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.