What do you do with highly correlated features?
There are multiple ways to deal with this problem. The easiest way is to delete or eliminate one of the perfectly correlated features. Another way is to use a dimension reduction algorithm such as Principle Component Analysis (PCA).
Should you drop highly correlated features?
For the model to be stable enough, the above variance should be low. If the variance of the weights is high, it means that the model is very sensitive to data. It means that the model might not perform well with test data. …
Why do we need to remove highly correlated variables?
In a more general situation, when you have two independent variables that are very highly correlated, you definitely should remove one of them because you run into the multicollinearity conundrum and your regression model’s regression coefficients related to the two highly correlated variables will be unreliable.
How many features is too many for XGBoost?
If you have 10,000,000 data points 150 features is not an issue, if you have 400 data points 150 features is way too much.
How to eliminate features with a high correlation?
4.1 Greedy Elimination The idea of this approach is to iteratively elimnate features with respect to their correlation to other features. Therefore, the feature pair with the highest absolute correlation coefficient is selected. The feature of this pair which has the lower correlation with the passengers’ survival is eliminated.
Do you randomly use correlated features in XGBoost?
It is still up to you to search for the correlated features to the one detected as important if you need to know all of them. To summarise, Xgboost does not randomly use the correlated features in each tree, which random forest model suffers from such a situation. Tianqi Chen, Michaël Benesty, Tong He. 2018.
What are some examples of perfectly correlated features?
Example: feature 1 has a gain of 0.8, feature 2 has a gain of 0.15, and feature 3|4 got a gain of 0.045 and 0.005 respectively. Feature 3 and 4 are perfectly correlated but were not picked at 50%|50% rate due to random sampling of features.
When to refocus on feature a or B in boosting?
In boosting, when a specific link between feature and outcome have been learned by the algorithm, it will try to not refocus on it (in theory it is what happens, the reality is not always that simple). Therefore, all the importance will be on feature A or on feature B (but not both).