Feature Selection in Machine Learning: Techniques | Methods

Feature Selection in Machine Learning: Imagine you are building a machine learning model with hundreds of variables, from age and income to zip codes and browsing patterns. Sounds powerful, right? But here’s the catch, too many features can actually confuse your model, make it slower, and even lower its accuracy. This is where feature selection in machine learning steps in.

Simply put, it’s the process of picking only the most useful input variables (features) for model training, while discarding the irrelevant or redundant ones. Think of it like trimming a tree, cutting away the excess branches so the core can grow stronger.

What is Feature Selection in Machine Learning?

At its heart, feature selection in machine learning is about identifying which variables (columns in your dataset) actually contribute to predicting your target variable.

For example, if you are predicting whether a customer will buy a product, features like age, salary, and purchase history may be useful, but something like customer ID or random token number won’t help at all. Including such useless variables not only increases noise but also wastes computational resources.

So, when we ask, “What is feature selection in machine learning?” the answer is: it’s the systematic way of choosing the right set of variables so your model performs better.

According to a paper published in the Journal of Machine Learning Research, “Feature selection reduces the risk of overfitting and enhances interpretability by eliminating irrelevant features.”

Why Do We Need Feature Selection?

You might be wondering, why not just feed all features into the algorithm and let it decide? Good question.

Here’s why we need feature selection:

1. Avoid Overfitting – Too many irrelevant variables make models memorize noise instead of learning patterns.
2. Improve Accuracy – Removing unnecessary data leads to cleaner, sharper predictions.
3. Reduce Training Time – Less data means faster computation.
4. Better Interpretability – Simpler models are easier to explain, especially in industries like healthcare and finance.

For example, in medical research, datasets often contain thousands of biomarkers. But not all of them are relevant to predicting a disease. Selecting the top 20–30 features significantly boosts performance while keeping the model explainable.

Feature Selection Techniques in Machine Learning

Now that we know why it’s important, let’s look at feature selection techniques in machine learning. These techniques fall broadly into three categories:

1. Filter Methods

Filter methods apply statistical tests to determine the relationship between each input feature and the target. They are fast and independent of the learning algorithm.

• Chi-Square Test – Checks how categorical features relate to the target.
• Correlation Coefficient – Identifies highly correlated variables.
• Information Gain & Mutual Information – Measures how much information a feature contributes.

2. Wrapper Methods

Here, features are selected based on how well they improve the performance of a specific model. They are more accurate but computationally expensive.

• Forward Selection – Start with no features and add one at a time.
• Backward Elimination – Start with all features and remove one at a time.
• Recursive Feature Elimination (RFE) – Iteratively remove the least important features.

3. Embedded Methods

These methods perform feature selection during model training. They are efficient and capture interactions between features.

• LASSO Regression (L1 regularization) – Shrinks coefficients of less important features to zero.
• Decision Trees & Random Forests – Naturally rank features based on importance.

Feature Subset Selection in Machine Learning

Another term you’ll often hear is feature subset selection in machine learning. While general feature selection may involve scoring or ranking variables, subset selection focuses on finding the optimal group of features that work best together.

Some common approaches are:

• Stepwise Selection – Combines forward and backward methods.
• Best Subset Selection – Evaluates all possible subsets (useful but computationally heavy).
• Heuristic Search – Uses algorithms like Genetic Algorithms or Simulated Annealing to search for the best subset.

This approach is especially useful when features are interdependent. For example, in credit scoring, income and employment type may individually seem weak, but together they provide strong predictive power.

Read More: Feature Extraction in Machine Learning: Meaning, Types & Techniques

Feature Selection Method in Machine Learning

Let’s dig deeper into specific feature selection methods in machine learning that are commonly applied:

1. Statistical Methods
   • ANOVA F-test
   • Pearson correlation
   • Chi-square test
2. Regularization Methods
   • LASSO (L1)
   • Ridge (L2, less for selection, more for shrinkage)
3. Tree-based Methods
   • Random Forest feature importance
   • XGBoost feature importance
4. Dimensionality Reduction vs. Feature Selection
   • PCA (Principal Component Analysis) reduces dimensions but creates new transformed features.
   • Feature selection keeps the original variables intact.

How to Do Feature Selection in Machine Learning?

Now comes the practical part: how to do feature selection in machine learning?

Here’s a step-by-step guide:

1. Understand Your Data – Explore the dataset, visualise correlations.
2. Apply Filter Methods First – Remove obviously irrelevant variables.
3. Use Wrapper Methods for Fine-Tuning – Apply RFE or forward selection.
4. Test with Embedded Methods – Train with models like LASSO or Random Forest to see feature importance.
5. Validate with Cross-Validation – Always check performance after feature removal.

Challenges in Feature Selection

While it sounds straightforward, there are some challenges:

• Curse of Dimensionality – With thousands of features, computation becomes costly.
• Correlation Among Features – Highly correlated variables may trick the selection process.
• Domain Knowledge – Sometimes statistical tests alone can’t decide; human expertise is essential.

How to do Feature Selection?

Here are some practical tips:

• Start with filter methods for speed.
• Use wrapper methods only when computational resources allow.
• Always cross-check results with validation datasets.
• Never rely solely on automated selection—domain understanding matters.
• Keep feature selection part of your pipeline to ensure reproducibility.

On A Final Note…

To sum it up, feature selection in machine learning is a must if you want to build efficient, accurate, and interpretable models. From simple filter techniques to advanced feature subset selection in machine learning, there are plenty of tools available to cut through noisy data.

Whether you use a statistical test, a feature selection method in machine learning like RFE, or embedded approaches like LASSO, the end goal remains the same: keep the useful variables, remove the unnecessary ones, and make your model smarter.

So next time you build a model, don’t just throw in every column from your dataset, ask yourself, “How to do feature selection in machine learning here?”