lale.lib.sklearn.linear_regression module¶

class lale.lib.sklearn.linear_regression.LinearRegression(*, fit_intercept=True, copy_X=True, n_jobs=None, positive=False)¶

Linear regression linear model from scikit-learn for classification.

This documentation is auto-generated from JSON schemas.

Parameters

fit_intercept (boolean, default True) – Whether to calculate the intercept for this model.
copy_X (boolean, default True) – If True, X will be copied; else, it may be overwritten.
n_jobs (union type, optional, not for optimizer, default None) –
The number of jobs to run in parallel.
- None
  
  1 unless in joblib.parallel_backend context.
- or -1
  
  Use all processors.
- or integer, >=1
  
  Number of CPU cores.
positive (boolean, optional, not for optimizer, default False) –
When set to True, forces the coefficients to be positive.

See also constraint-1.

Notes

constraint-1 : union type

Setting positive=True is only supported for dense arrays.

positive : False

or negated type of ‘X/isSparse’

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) – Features; the outer array is over samples.
y (union type) –
Target values. Will be cast to X’s dtype if necessary
- array of items : array of items : float
- or array of items : float
sample_weight (union type, optional) –
Sample weights.
- array of items : float
- or None
  
  Samples are equally weighted.

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) – Samples.

Returns

result – Returns predicted values.

Return type

union type