Feature selection methods for dummies

Data is everywhere, and with the rapid growth of technology, we’re generating more of it every day. Feature selection is a crucial step in the process of making sense of that data, particularly when it comes to building machine learning models.

Don’t worry if you’re new to this – you’ve come to the right place! In this article, we’ll break down feature selection methods into simple, easy-to-understand concepts so you can get started on your data analysis journey.

What is Feature Selection?

In machine learning, we use data to train algorithms to make predictions or decisions. This data usually contains multiple attributes or features, which can be anything from a person’s age to a product’s price.

However, not all features are equally important or relevant to the problem we’re trying to solve. Feature selection is the process of identifying and selecting the most important features from the data, resulting in a more accurate and efficient model.

Why is Feature Selection Important?

Feature selection is crucial for several reasons:

  1. Reduces complexity: By selecting only the most important features, we can reduce the complexity of our model, making it easier to understand and interpret.
  2. Improves accuracy: Removing irrelevant or redundant features can reduce noise in the data and improve the accuracy of our predictions.
  3. Reduces training time: Using fewer features can speed up the training process, saving both time and computational resources.

Feature Selection Methods

There are three main categories of feature selection methods: filter methods, wrapper methods, and embedded methods.

Filter Methods

Filter methods evaluate each feature independently, based on its relationship with the target variable. These methods are relatively fast and simple, making them a popular choice for initial feature selection. Some common filter methods include:

  • Variance Threshold: Removes features with low variance, as they don’t contribute much to the model’s predictive power.
  • Correlation Coefficient: Measures the strength of the relationship between each feature and the target variable, selecting only those with a strong correlation.
  • Mutual Information: Quantifies the amount of information one feature provides about the target variable.

Wrapper Methods

Wrapper methods take a more comprehensive approach, evaluating subsets of features based on their performance in a specific machine learning algorithm. Some popular wrapper methods include:

  • Forward Selection: Starts with an empty set of features and iteratively adds the best-performing feature at each step until the desired number of features is reached.
  • Backward Elimination: Starts with all features and iteratively removes the least significant feature until the desired number of features is reached.
  • Recursive Feature Elimination (RFE): Combines forward selection and backward elimination, iteratively adding and removing features to find the optimal subset.

Embedded Methods

Embedded methods integrate feature selection into the machine learning algorithm itself, selecting features based on their importance during the model training process. Some examples of embedded methods include:

  • LASSO Regression: A linear regression technique that uses regularization to shrink the coefficients of less important features to zero, effectively removing them from the model.
  • Decision Trees: Algorithms like Random Forests or Gradient Boosted Trees automatically rank features based on their importance in splitting the data at each node.


Feature selection is a critical step in the data analysis process, helping us to focus on the most important aspects of our data and build more accurate, efficient, and interpretable machine learning models.

By understanding the basics of filter, wrapper, and embedded methods, you’ll be well on your way to making sense of your data and uncovering valuable insights.

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