Hands-On Machine Learning with C++

Feature selection methods

Feature selection methods don't change the initial values of the variables or features; instead, they remove particular features from the source dataset that aren't relevant. Some of the feature selection methods we can use are as follows:

• Missing value ratio: This method is based on the idea that a feature that misses many values should be eliminated from a dataset because it doesn't contain valuable information and can distort the model's performance results. So, if we have some criteria for identifying missing values, we can calculate their ratio to typical values and set a threshold that we can use to eliminate features with a high missing value ratio.
• Low variance filter: This method is used to remove features ...