Question

Greedy Backwards pseudo-code:

1. Train a model on all M features
2. Train models on all M-1 subsets
3. Drop the feature whose removal resulted in the smallest decrease in performance
Repeat steps 1-3 until:
desired number of features or
the performance degrades beyond the desired threshold

Answer 1

The Greedy Backwards algorithm is a machine learning feature selection method that starts with all features and iteratively removes the least important one until a desired condition is met.

Step-by-step explanation:

The Greedy Backwards algorithm described in the question is a type of feature selection method used in machine learning. The general idea is to begin with a model that uses all available features (or predictors) and iteratively remove the least important feature—the one that when dropped, causes the smallest decrease in model performance. This process is repeated until a specified number of features remains, or the model's performance declines beyond what's acceptable.

The pseudo-code provided outlines the steps of this Greedy Backwards feature elimination process: Training a full model, evaluating models with one feature removed, and dropping the feature that has the least impact on performance. This cycle continues until you reach the desired feature count or performance threshold.