lale.lib.autogen.ridge_classifier_cv module¶
- class lale.lib.autogen.ridge_classifier_cv.RidgeClassifierCV(*, alphas=['0.1', '1.0', '10.0'], fit_intercept=True, scoring=None, cv=None, class_weight=None, store_cv_values=False)¶
Bases:
PlannedIndividualOp
Combined schema for expected data and hyperparameters.
This documentation is auto-generated from JSON schemas.
- Parameters
alphas (array, not for optimizer, default [0.1, 1.0, 10.0] of items : float) – Array of alpha values to try
fit_intercept (boolean, default True) – Whether to calculate the intercept for this model
scoring (union type, default None) –
A string (see model evaluation documentation) or a scorer callable object / function with signature
scorer(estimator, X, y)
.callable, not for optimizer
or ‘accuracy’ or None
cv (union type, default None) –
- Cross-validation as integer or as object that has a split function.
The fit method performs cross validation on the input dataset for per trial, and uses the mean cross validation performance for optimization. This behavior is also impacted by handle_cv_failure flag. If integer: number of folds in sklearn.model_selection.StratifiedKFold. If object with split function: generator yielding (train, test) splits as arrays of indices. Can use any of the iterators from https://scikit-learn.org/stable/modules/cross_validation.html#cross-validation-iterators.
integer, >=1, >=3 for optimizer, <=4 for optimizer, uniform distribution, default 5
or Any, not for optimizer
or None
See also constraint-2.
class_weight (union type, default None) –
Weights associated with classes in the form
{class_label: weight}
’balanced’
or None
store_cv_values (boolean, default False) –
Flag indicating if the cross-validation values corresponding to each alpha should be stored in the
cv_values_
attribute (see below)See also constraint-2.
Notes
constraint-1 : any type
constraint-2 : union type
cv!=None and store_cv_values=True are incompatible
cv : None
or store_cv_values : False
- decision_function(X)¶
Confidence scores for all classes.
Note: The decision_function method is not available until this operator is trained.
Once this method is available, it will have the following signature:
- Parameters
X (union type) –
Samples.
array of items : Any
or array of items : array of items : float
- Returns
result – Confidence scores per (sample, class) combination
- Return type
Any
- fit(X, y=None, **fit_params)¶
Train the operator.
Note: The fit method is not available until this operator is trainable.
Once this method is available, it will have the following signature:
- Parameters
X (array of items : array of items : float) – Training vectors, where n_samples is the number of samples and n_features is the number of features.
y (array of items : float) – Target values
sample_weight (union type, optional) –
Sample weight.
float
or array of items : float
- predict(X, **predict_params)¶
Make predictions.
Note: The predict method is not available until this operator is trained.
Once this method is available, it will have the following signature:
- Parameters
X (union type) –
Samples.
array of items : Any
or array of items : array of items : float
- Returns
result – Predicted class label per sample.
- Return type
array of items : float