error_measure module¶
Error measure¶
The error_measure module implements
the concept of error measures for machine learning algorithms.
This concept is implemented through the MLErrorMeasure class
and implements the different evaluation methods.
The error measure class is adapted for supervised machine learning algorithms, as it measures the error of a predicted value to some reference value.
-
class
gemseo.mlearning.qual_measure.error_measure.MLErrorMeasure(algo)[source]¶ Bases:
gemseo.mlearning.qual_measure.quality_measure.MLQualityMeasureError measure for machine learning.
Constructor.
- Parameters
algo (MLAlgo) – machine learning algorithm.
-
evaluate_bootstrap(n_replicates=100, multioutput=True)[source]¶ Evaluate quality measure using the bootstrap technique.
- Parameters
n_replicates (int) – number of bootstrap replicates. Default: 100.
multioutput (bool) – if True, return the error measure for each output component. Otherwise, average these errors. Default: True.
- Returns
quality measure value.
-
evaluate_kfolds(n_folds=5, multioutput=True)[source]¶ Evaluate quality measure using the k-folds technique.
- Parameters
n_folds (int) – number of folds. Default: 5.
multioutput (bool) – if True, return the error measure for each output component. Otherwise, average these errors. Default: True.
- Returns
quality measure value.
-
evaluate_learn(multioutput=True)[source]¶ Evaluate quality measure using the learning dataset.
- Parameters
multioutput (bool) – if True, return the error measure for each output component. Otherwise, average these errors. Default: True.
- Returns
quality measure value.
-
evaluate_test(test_data, multioutput=True)[source]¶ Evaluate quality measure using a test dataset.
- Parameters
test_data (Dataset) – test data.
multioutput (bool) – if True, return the error measure for each output component. Otherwise, average these errors. Default: True.
- Returns
quality measure value.