mlens.metrics package¶
Submodules¶
Module contents¶
ML-ENSEMBLE
author: | Sebastian Flennerhag |
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copyright: | 2017 |
license: | MIT |
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mlens.metrics.
rmse
(y, p)[source]¶ Root Mean Square Error.
\[RMSE(\mathbf{y}, \mathbf{p}) = \sqrt{MSE(\mathbf{y}, \mathbf{p})},\]with
\[MSE(\mathbf{y}, \mathbf{p}) = |S| \sum_{i \in S} (y_i - p_i)^2\]Parameters: - y (array-like of shape [n_samples, ]) – ground truth.
- p (array-like of shape [n_samples, ]) – predicted labels.
Returns: z – root mean squared error.
Return type: float
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mlens.metrics.
mape
(y, p)[source]¶ Mean Average Percentage Error.
\[MAPE(\mathbf{y}, \mathbf{p}) = |S| \sum_{i \in S} | \frac{y_i - p_i}{y_i} |\]Parameters: - y (array-like of shape [n_samples, ]) – ground truth.
- p (array-like of shape [n_samples, ]) – predicted labels.
Returns: z – mean average percentage error.
Return type: float
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mlens.metrics.
wape
(y, p)[source]¶ Weighted Mean Average Percentage Error.
\[WAPE(\mathbf{y}, \mathbf{p}) = \frac{\sum_{i \in S} | y_i - p_i|}{ \sum_{i \in S} |y_i|}\]Parameters: - y (array-like of shape [n_samples, ]) – ground truth.
- p (array-like of shape [n_samples, ]) – predicted labels.
Returns: z – weighted mean average percentage error.
Return type: float
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mlens.metrics.
make_scorer
(score_func, greater_is_better=True, needs_proba=False, needs_threshold=False, **kwargs)[source]¶ Make a scorer from a performance metric or loss function.
This factory function wraps scoring functions for use in GridSearchCV and cross_val_score. It takes a score function, such as
accuracy_score
,mean_squared_error
,adjusted_rand_index
oraverage_precision
and returns a callable that scores an estimator’s output.Read more in the User Guide.
Parameters: - score_func (callable,) – Score function (or loss function) with signature
score_func(y, y_pred, **kwargs)
. - greater_is_better (boolean, default=True) – Whether score_func is a score function (default), meaning high is good, or a loss function, meaning low is good. In the latter case, the scorer object will sign-flip the outcome of the score_func.
- needs_proba (boolean, default=False) – Whether score_func requires predict_proba to get probability estimates out of a classifier.
- needs_threshold (boolean, default=False) –
Whether score_func takes a continuous decision certainty. This only works for binary classification using estimators that have either a decision_function or predict_proba method.
For example
average_precision
or the area under the roc curve can not be computed using discrete predictions alone. - **kwargs (additional arguments) – Additional parameters to be passed to score_func.
Returns: scorer – Callable object that returns a scalar score; greater is better.
Return type: callable
Examples
>>> from sklearn.metrics import fbeta_score, make_scorer >>> ftwo_scorer = make_scorer(fbeta_score, beta=2) >>> ftwo_scorer make_scorer(fbeta_score, beta=2) >>> from sklearn.model_selection import GridSearchCV >>> from sklearn.svm import LinearSVC >>> grid = GridSearchCV(LinearSVC(), param_grid={'C': [1, 10]}, ... scoring=ftwo_scorer)
- score_func (callable,) – Score function (or loss function) with signature