lale.lib.sklearn.ada_boost_regressor module¶
- class lale.lib.sklearn.ada_boost_regressor.AdaBoostRegressor(*, n_estimators=50, learning_rate=1.0, loss='linear', random_state=None, estimator=None)¶
Bases:
PlannedIndividualOp
AdaBoost regressor from scikit-learn for boosting ensemble.
This documentation is auto-generated from JSON schemas.
- Parameters
n_estimators (integer, >=50 for optimizer, <=500 for optimizer, uniform distribution, default 50) – The maximum number of estimators at which boosting is terminated.
learning_rate (float, >=0.01 for optimizer, <=1.0 for optimizer, loguniform distribution, default 1.0) – Learning rate shrinks the contribution of each regressor by
loss (‘linear’, ‘square’, or ‘exponential’, default ‘linear’) – The loss function to use when updating the weights after each
random_state (union type, not for optimizer, default None) –
If int, random_state is the seed used by the random number generator;
integer
or numpy.random.RandomState
or None
estimator (union type, optional, not for optimizer, default None) –
The base estimator to fit on random subsets of the dataset.
operator
or None
See also constraint-1.
Notes
constraint-1 : union type
Only estimator or base_estimator should be specified. As base_estimator is deprecated, use estimator.
base_estimator : False or ‘deprecated’
or estimator : None
- 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) – The training input samples. Sparse matrix can be CSC, CSR, COO,
y (array of items : float) – The target values (real numbers).
sample_weight (union type, optional, default None) –
Sample weights. If None, the sample weights are initialized to
array of items : float
or None
- 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 (array, optional of items : array of items : float) – The training input samples. Sparse matrix can be CSC, CSR, COO,
- Returns
result – The predicted regression values.
- Return type
array of items : float