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What does a negative feature importance mean?
0. Negative feature importance value means that feature makes the loss go up. This means that your model is not getting good use of this feature.
How feature importance is calculated in CatBoost?
To get this feature importance, catboost simply takes the difference between the metric (Loss function) obtained using the model in normal scenario (when we include the feature) and model without this feature (model is built approximately using the original model with this feature removed from all the trees in the …
How is the catboost function used in model interpretation?
To get this feature importance, catboost simply takes the difference between the metric (Loss function) obtained using the model in normal scenario (when we include the feature) and model without this feature (model is built approximately using the original model with this feature removed from all the trees in the ensemble).
When to use lossfunctionchange or predictionvalueschange?
Though both PredictionValuesChange & LossFunctionChange can be used for all types of metrics, it is recommended to use LossFunctionChange for ranking metrics. Except for PredictionValuesChange , all other methods can use test data to find feature importance using models trained on train data.
What can catboost be used for in data science?
Catboost can be used to solve regression, classification and ranking problems. As Data Scientists, we can easily train models and make predictions, but, we often fail to understand what’s happening inside those fancy algorithms.
How to calculate the importance of a feature?
Calculate the feature importances ( Feature importance and Feature interaction strength ). The name of the resulting file that contains regular feature importance data (see Feature importance ). Set the required file name for further feature importance analysis.