MultiOutputRegressor
¶
-
class
ibex.sklearn.multioutput.
MultiOutputRegressor
(estimator, n_jobs=1)¶ Bases:
sklearn.multioutput.MultiOutputRegressor
,ibex._base.FrameMixin
Note
The documentation following is of the class wrapped by this class. There are some changes, in particular:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
Multi target regression
This strategy consists of fitting one regressor per target. This is a simple strategy for extending regressors that do not natively support multi-target regression.
- estimator : estimator object
- An estimator object implementing fit and predict.
- n_jobs : int, optional, default=1
- The number of jobs to run in parallel for fit. If -1, then the number of jobs is set to the number of cores. When individual estimators are fast to train or predict using n_jobs>1 can result in slower performance due to the overhead of spawning processes.
-
fit
(X, y, sample_weight=None)¶ Note
The documentation following is of the class wrapped by this class. There are some changes, in particular:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
- Fit the model to data.
Fit a separate model for each output variable.
- X : (sparse) array-like, shape (n_samples, n_features)
- Data.
- y : (sparse) array-like, shape (n_samples, n_outputs)
- Multi-output targets. An indicator matrix turns on multilabel estimation.
- sample_weight : array-like, shape = (n_samples) or None
- Sample weights. If None, then samples are equally weighted. Only supported if the underlying regressor supports sample weights.
- self : object
- Returns self.
- A parameter
-
partial_fit
(X, y, sample_weight=None)[source]¶ Note
The documentation following is of the class wrapped by this class. There are some changes, in particular:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
- Incrementally fit the model to data.
Fit a separate model for each output variable.
- X : (sparse) array-like, shape (n_samples, n_features)
- Data.
- y : (sparse) array-like, shape (n_samples, n_outputs)
- Multi-output targets.
- sample_weight : array-like, shape = (n_samples) or None
- Sample weights. If None, then samples are equally weighted. Only supported if the underlying regressor supports sample weights.
- self : object
- Returns self.
- A parameter
-
predict
(X)¶ Note
The documentation following is of the class wrapped by this class. There are some changes, in particular:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
- Predict multi-output variable using a model
- trained for each target variable.
- X : (sparse) array-like, shape (n_samples, n_features)
- Data.
- y : (sparse) array-like, shape (n_samples, n_outputs)
- Multi-output targets predicted across multiple predictors. Note: Separate models are generated for each predictor.
- A parameter
-
score
(X, y, sample_weight=None)[source]¶ Note
The documentation following is of the class wrapped by this class. There are some changes, in particular:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
Returns the coefficient of determination R^2 of the prediction.
The coefficient R^2 is defined as (1 - u/v), where u is the residual sum of squares ((y_true - y_pred) ** 2).sum() and v is the regression sum of squares ((y_true - y_true.mean()) ** 2).sum(). 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.
R^2 is calculated by weighting all the targets equally using multioutput=’uniform_average’.
- X : array-like, shape (n_samples, n_features)
- Test samples.
- y : array-like, shape (n_samples) or (n_samples, n_outputs)
- True values for X.
- sample_weight : array-like, shape [n_samples], optional
- Sample weights.
- score : float
- R^2 of self.predict(X) wrt. y.
- A parameter
- A parameter