Contents
What is feature ratio?
This is calculated by taking the ratio of two other raw features: number of clicks / number of ads shown. Generally speaking, engineering more, especially meaningful, features is useful for any machine learning model.
How is feature importance calculated Xgboost?
Importance is calculated for a single decision tree by the amount that each attribute split point improves the performance measure, weighted by the number of observations the node is responsible for. The feature importances are then averaged across all of the the decision trees within the model.
What is gain ratio in feature selection?
Gain ratio (GR) is a modification of the information gain that reduces its bias. Gain ratio takes number and size of branches into account when choosing an attribute. It corrects the information gain by taking the intrinsic information of a split into account.
Why is error ratio used instead of feature importance?
A positive aspect of using the error ratio instead of the error difference is that the feature importance measurements are comparable across different problems. The importance measure automatically takes into account all interactions with other features.
Why do we need to use feature importance measures?
One thing to note is that the more accurate our model is, the more we can trust feature importance measures and other interpretations. I assume that the model we build is reasonably accurate (as each data scientist will strive to have such a model) and in this article, I focus on the importance measures.
How to determine the feature importance of a variable?
It will automatically “select the most important features” for the problem at hand. Now, if we do not want to follow the notion for regularisation (usually within the context of regression), random forest classifiers and the notion of permutation tests naturally lend a solution to feature importance of group of variables.
Why is the interaction between two features important?
This is also a disadvantage because the importance of the interaction between two features is included in the importance measurements of both features. This means that the feature importances do not add up to the total drop in performance, but the sum is larger.