Guide Explainable AI
Feature Importance: Methods, Tools, and Best Practices
Feature importance is a technique used in machine learning that assigns a score to input features based on how useful they are in predicting a target variable.
Feature Importance: Methods, Tools, and Best Practices

What Is Feature Importance?

Feature importance is a technique used in machine learning that assigns a score to input features based on how useful they are in predicting a target variable. These scores provide a ranking, allowing you to understand which features are most influential in your model’s prediction.

For example, consider a model that predicts the price of a house based on various features such as the number of rooms, the location, the age of the house, and so on. Feature importance would help you determine which of these factors has the most significant impact on the price.

Feature importance illuminates the relationship between the features and the target variable, providing valuable insights into your data. It’s a useful tool for machine learning and data analysis, and is a key component in explainable AI (XAI) strategies.

How Is Feature Importance Related to Feature Selection?

Feature selection is a process where you automatically or manually select features in your data that contribute most to a prediction or output. In machine learning, having irrelevant features in your data can decrease the accuracy of your models and make your model learn based on irrelevant features.

Feature importance helps in this process by ranking the features based on their importance. The less important features can be eliminated without affecting the accuracy of the model significantly. This can also reduce the amount of data and computational power required to train the model.

Why Is Feature Importance Useful in Machine Learning?

Feature importance holds many practical use cases in machine learning, including the following:

Improving Model Performance

By focusing on the most important features and discarding the irrelevant ones, you can reduce overfitting, improve accuracy, and reduce training time. Additionally, using feature importance can help in addressing issues like multicollinearity where predictors are correlated with each other. By identifying and removing these redundant features, you can enhance your model’s performance.

Model Interpretability

Interpreting a machine learning model’s decisions and predictions can be difficult, especially for complex models. Feature importance can aid in this process by providing a ranking of the features based on their contribution to the model’s prediction. This can be useful in explaining the model to stakeholders.

Feature importance helps make your model more transparent. Understanding which features are driving the predictions can help in validating the model and ensuring it is making decisions for the right reasons.

Business Decision-Making

By understanding which features are most important in predicting a certain outcome, businesses can focus their resources and strategies on these areas. For instance, if a retail company finds that the most important feature affecting sales is the training received by salespeople, they might decide to provide more extensive training programs.

Key Feature Importance Methods

There are several methods to calculate feature importance, each with its own advantages and limitations.

1. Single-Variable Prediction

Single-variable prediction, also known as univariate feature selection, is a method where you create a simple model for each feature, using that feature alone to make predictions. The performance of these models gives an indication of the importance of each feature.

The advantage of this method is its simplicity and ease of interpretation. However, it fails to capture interactions between features and can be biased if the number of features is large compared to the number of observations.

2. Permutation

Permutation feature importance involves shuffling the values of a feature and measuring how much the performance drops. The idea is that if a feature is important, shuffling its values will degrade model performance.

This method takes into account interactions between features and does not require retraining the model. However, it can be computationally expensive and might be biased if features are correlated.

3. Linear Regression

Linear regression is a statistical method that uses a linear function to describe the relationship between your target variable and one or more predictor variables. In the context of feature importance, coefficients derived from the linear regression model can be used to rank the features. Larger absolute values of these coefficients indicate more importance. However, it’s important to be careful when interpreting these coefficients, especially with multi-collinearity.

4. Logistic Regression

Logistic regression, similar to linear regression, is a predictive analysis technique. It is used when the dependent variable is categorical. For instance, predicting whether an email is spam or not. In logistic regression, the coefficients can also be used to infer feature importance. A feature is considered important if its coefficient in the logistic regression model is larger (in absolute value) and is statistically significant.

5. Decision Trees

Decision trees work by splitting the dataset into subsets based on different conditions, and these decisions help in predicting the target variable. The importance of a feature can be determined by the number of times a feature is used to split the data. The greater the number of times a feature is used for splitting, the higher its importance. However, these methods can be prone to overfitting if not implemented correctly.

6. Gini Importance (Random Forest)

Gini importance, also known as Mean Decrease Impurity, is a method used in random forest models. It calculates feature importance as the total decrease in node impurities from splitting on the feature, averaged over all trees in the model.

The advantage of this method is that it takes into account interaction effects and can handle different types of features. However, it can be biased towards features with more categories and might overestimate importance if features are correlated.

7. Neural Networks

Neural networks are a form of machine learning models known for their ability to learn complex patterns. They consist of several layers of nodes, where each node is a simple, non-linear processing unit. Feature importance in neural networks can be complex due to their non-linear nature and the large number of nodes, up to billions in some neural architectures.

Techniques like permutation importance, partial dependence plots, and LIME can be used to estimate feature importance in these models.

Tools and Libraries for Feature Importance

There are several tools and libraries available that can help you make use of feature importance.

SHAP Library

SHAP (SHapley Additive exPlanations) is a game theory approach to explain the output of any machine learning model. It connects optimal credit allocation with local explanations using the classic Shapley values from game theory and their related extensions. It provides a unified measure of feature importance that works with any model. SHAP values not only give you the importance of features but also the direction of that importance (positive or negative).


Scikit-learn is a popular machine learning library in Python. It provides several methods to calculate feature importance. For instance, in the case of decision trees, random forests, and gradient boosting machines, it calculates feature importance based on the mean decrease in impurity (MDI). For linear and logistic regression, coefficients can be used to determine feature importance.


ELI5 (Explain Like I’m 5) is another Python library that allows you to visualize and debug various machine learning models using a unified API. It has built-in support for several popular machine learning libraries and provides a way to compute feature importance for any black-box estimator by measuring feature importance using feature permutation.


XGBoost stands for eXtreme Gradient Boosting. It is a popular implementation of the gradient-boosting decision trees algorithm, used for classification, regression, and ranking problems. XGBoost provides a straightforward way to calculate feature importance based on the number of times a feature appears in a tree across all the trees in the model. It also provides another method known as ‘gain’, which calculates the average gain of a feature when it is used in trees.

Related content: Read our guide to explainable AI tools (coming soon)

Best Practices for Effective Feature Importance Measurement

Use Multiple Methods

Feature importance is not calculated using a single, universal method. Instead, there are several different techniques available, each with its own strengths. It’s important to use multiple methods to get a comprehensive understanding of your data:

  • Tree-based methods can help understand complex, non-linear relationships between features.
  • Linear methods like logistic regression or linear regression can provide a picture of linear relationships between features.
  • Permutation importance provides a more holistic measure of feature importance, but should be used sparingly because it is computationally expensive.

Handle Imbalanced Data

Imbalanced data is a common challenge in machine learning. This refers to situations where the classes in the target variable are not equally represented. For instance, in a dataset of credit card transactions, the number of fraudulent transactions (a minority class) is likely to be much smaller than the number of legitimate transactions (a majority class).

When dealing with imbalanced data, it’s important to take extra care when determining feature importance. If you don’t, your model might overemphasize the majority class and underestimate the importance of features that are significant for the minority class.

There are several strategies you can use to handle imbalanced data. One approach is to use sampling techniques, such as oversampling the minority class or undersampling the majority class. Another approach is to use cost-sensitive methods, which assign a higher penalty for misclassifying the minority class.

Check for Multicollinearity

Multicollinearity refers to a situation where two or more features are highly correlated. They provide the same or similar information about the target variable. This can cause problems when determining feature importance, as it can be hard to disentangle the effects of correlated features.

To avoid this, it’s important to check for multicollinearity in your data. You can do this by calculating the variance inflation factor (VIF) for each feature. A high VIF indicates that a feature is highly correlated with other features. If you find multicollinearity in your data, two options are to remove one of the correlated features, or aggregate/combine the correlated features into one.

Use Domain Knowledge

While machine learning algorithms can discover complex patterns in data, they can’t replace domain knowledge. Domain knowledge refers to your understanding of the field from which the data was collected. This can include knowledge about the relationships between features, the meaning of different feature values, and the context in which the data was collected.

Using domain knowledge can help you interpret the results of feature importance analysis. For instance, if a feature is deemed important by an algorithm but doesn’t make sense in the context of your domain, you might want to investigate further.


Bootstrapping is a statistical technique that can help you estimate the uncertainty of your feature importance scores. It involves creating many resamples of your data, calculating the feature importance scores for each resample, and then looking at the distribution of these scores.

With this approach, you can get a better sense of the variability in your feature importance scores. This can help you determine which features are consistently important across different resamples, and which features’ importance scores are more dependent on the specific sample of data you have.

Testing and Evaluating Model Features with Kolena

We built Kolena to make robust and systematic ML testing easy and accessible for all organizations. With Kolena, machine learning engineers and data scientists can uncover hidden machine learning model behaviors, easily identify gaps in the test data coverage, and truly learn where and why a model is underperforming, all in minutes not weeks. Kolena’s AI / ML model testing and validation solution helps developers build safe, reliable, and fair systems by allowing companies to instantly stitch together razor-sharp test cases from their data sets, enabling them to scrutinize AI/ML models in the precise scenarios those models will be unleashed upon the real world. Kolena platform transforms the current nature of AI development from experimental into an engineering discipline that can be trusted and automated.

Among its many capabilities, Kolena also helps with feature importance evaluation, and allows auto-tagging features. It can also display the distribution of various features in your datasets.

Reach out to us to learn how the Kolena platform can help build a culture of AI quality for your team.