Recursive Feature Elimination

Recursive Feature Elimination is an iterative method used to select features by repeatedly building a model and removing the weakest feature at each step. In financial modeling, this helps identify the most predictive variables by seeing which ones contribute the least to the model's performance.

By pruning the feature set down to the most significant indicators, developers can build leaner and more effective trading algorithms. This process is repeated until the desired number of features is reached or the model's performance degrades.

It is a powerful way to ensure that only the most robust signals are driving the strategy. It effectively automates the search for the best market predictors.