In my machine learning projects, feature selection is a crucial step that I take seriously, as it can significantly impact the model’s performance and interpretability.
My approach usually begins with a strong understanding of the domain and the data itself. I make it a point to explore the dataset thoroughly using visualizations, descriptive statistics, and correlation matrices.
This helps me get an intuitive feel for which features might be relevant and which ones could be redundant or noisy.
Once I have a general idea, I start with removing features that are obviously irrelevant for example, IDs or columns with a high percentage of missing values that don’t contribute meaningfully.
I also look out for features with low variance, as they typically add little value to the model. After that, I perform correlation analysis to detect multicollinearity.
If two features are highly correlated, I usually retain the one that has a stronger relationship with the target variable and drop the other.
Next, I move to more systematic methods. I often use feature importance techniques based on tree-based algorithms like Random Forest or XGBoost. These models provide a good measure of which features contribute most to the predictive power. In some cases, I apply Recursive Feature Elimination (RFE) to iteratively eliminate the least important features and assess the impact on model accuracy.
Additionally, I rely on domain knowledge wherever possible. There are times when a feature might not seem statistically important but has known practical relevance in such cases, I choose to keep it and observe its effect during model validation. Finally, I validate my selected feature set using cross-validation to ensure the model generalizes well and avoids overfitting. This blend of intuition, statistics, and algorithmic selection helps me craft efficient and effective models.