55. Multiple Regression: More Features, More Power (And More Ways to Break Things)

The process of selecting relevant features in machine learning is crucial for improving model performance, reducing overfitting, and enhancing interpretability. The two methods you've mentioned—correlation with the target variable and using SelectKBest—are effective strategies to identify important features.

Method 1: Correlation with Target Variable

This method involves calculating the correlation between each feature and the target variable (in this case, house prices). Features that have a strong positive or negative correlation are likely to be more relevant for predicting house prices. Here's how you can implement it:

python
 
1import pandas as pd
2from sklearn.datasets import fetch_california_housing
3
4# Load dataset
5housing = fetch_california_housing()
6X = housing.data
7y = housing.target
8
9# Calculate absolute correlations with the target variable
10correlations = X.corrwith(pd.Series(y, name='price')).abs().sort_values(ascending=False)
11print("Correlation with target:")
12print(correlations)

Method 2: Using SelectKBest for Feature Selection

The SelectKBest method from scikit-learn automatically selects the top k features based on a specified scoring function. For regression tasks, you can use the F-stat

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