Why we 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. The weights differ largely with training data if the variance is high.
Is high correlation good for machine learning?
Correlation can be an important tool for feature engineering in building machine learning models. Predictors which are uncorrelated with the objective variable are probably good candidates to trim from the model (shoe size is not a useful predictor for salary).
What is the correlation score for feature correlation?
Each of those correlation types can exist in a spectrum represented by values from 0 to 1 where slightly or highly positive correlation features can be something like 0.5 or 0.7. If there is a strong and perfect positive correlation, then the result is represented by a correlation score value of 0.9 or 1.
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.
How are feature pairs with high correlations arranged?
High positive correlation are shown in blue, high negative correlations in red, while white represents no correlation between two features. The features are ordered with respect to their correlation coefficient to survival from left to right. Lets have a look at the ten feature pairs with the highes correlations:
Are there any algorithms that benefit from correlation?
Some algorithms like Naive Bayes actually directly benefit from “positive” correlated features. And others like random forest may indirectly benefit from them. Imagine having 3 features A, B, and C. A and B are highly correlated to the target and to each other, and C isn’t at all.