Which method is best for feature selection according to analysis results?

Lasso regression is a preferred method for feature selection primarily due to its ability to perform both variable selection and regularization simultaneously. This technique works by adding a penalty equal to the absolute value of the magnitude of coefficients. In doing so, Lasso can shrink some coefficients to zero, effectively excluding those features from the model. This property makes Lasso regression particularly effective at handling datasets with a large number of features, allowing for a more interpretable model that includes only the most significant variables.

The effectiveness of Lasso regression in feature selection is especially valuable in high-dimensional spaces where many features may be irrelevant or redundant. By selecting a simpler model with fewer predictors, it not only enhances predictive performance but also helps in avoiding overfitting, which is a common issue when too many irrelevant features are included.

While Ridge regression also includes a penalty that can handle multicollinearity, it does not perform feature selection in the same way, as it tends to retain all features by shrinking their coefficients rather than setting them to zero. Logistic regression is primarily a classification technique and does not inherently address feature selection, and K-means clustering is an unsupervised learning method focused on grouping data points rather than selecting features. Thus, Lasso regression stands out as the optimal