Multiclass pipeline #21
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jeremyliweishih
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Aug 23, 2019
jeremyliweishih
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Aug 23, 2019
jeremyliweishih
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Aug 23, 2019
kmax12
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Aug 24, 2019
kmax12
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Aug 27, 2019
kmax12
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Sep 9, 2019
kmax12
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kmax12
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Sep 10, 2019
kmax12
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Sep 10, 2019
…evalml into multiclass_pipeline
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Added multiclass functionality for classification models, updated metrics and included tests.
Does not include changes for feature-importance
However, I don't think we would need to change the feature_importance functions for logistic regression, RF or XGClassifier as they're are not one. vs all classifiers. For LR, the loss function is changed to multinomial logistic loss for multiclass classification, RF inherently works with multiple classes, and XGClassifier uses softmax loss.Upon further review, there will need to be changes for feature importance.Feature importance has been added for LR. Changes will not be needed for RF or XGClassifier.
Metrics
Currently for classification metrics, I am using themicroparameter which uses the global count to define metrics. In the case of precision, the total count of FP etc is used. Themacroparameter would calculate FP etc. per label and then use the average. I think either parameter could work and its more of a design choice.Separate metrics have been created for each type of averaging.