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This article introduces what the forward stepwise regression in R language is like, the content is very detailed, interested friends can use for reference, I hope it can be helpful to you.

In the process of modeling, selecting an appropriate feature set can help control the complexity of the model and prevent over-fitting and other problems. In order to select the best feature set, we can traverse all the column combinations to find the set with the best effect, but this requires a lot of computation. The forward stepwise regression method introduced in this paper is aimed at the modification of the least square method. Compared with going through all the combinations, forward stepwise regression can greatly save the amount of calculation and select the optimal feature set, so as to solve the problem of over-fitting. "

Forward stepwise regression

The process of forward stepwise regression is to traverse a subset of attributes and select the column of attributes that works best for the model. Then look for the second column attribute that works best with it, rather than traversing all two column subsets. And so on, each traversal, the subset contains the optimal subset obtained from the last traversal. In this way, each traversal will select a new attribute to be added to the feature set, until the number of features in the feature set can no longer be increased.

Instance code

1. Data import and grouping. Import the data, extract 70% of the data set as the training set, and the remaining 30% as the test set. The features are stored separately from the label.

Target.url

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