How to visualise XgBoost model feature importance in Python
XGBoost is a powerful and popular library for gradient boosting in Python. One of the key advantages of XGBoost is its ability to handle large datasets and high-dimensional data. One of the features of XGBoost is the ability to understand feature importance.
Feature importance is a measure of how much each feature contributes to the model’s predictions. It can help you understand which features are most important for your model and make informed decisions about which features to keep or remove from your dataset.
In XGBoost, feature importance is calculated using the built-in feature_importances_ attribute. Once the model is trained, you can access this attribute to get the feature importance scores. These scores are returned in the form of a numpy array, where each element corresponds to a feature in your dataset.
You can also use the plot_importance() function provided by XGBoost to visualize the feature importance. This function creates a bar chart where each bar represents a feature, and the height of the bar corresponds to the feature’s importance score. This function allows you to see which features are most important at a glance.
You can use the xgboost.plot_importance() function to plot the feature importance, you can specify the model, importance_type and the number of features to be plotted. importance_type can be either ‘weight’, ‘gain’ or ‘cover’ where weight represents the number of times a feature appears in a tree, gain represents the average gain of the feature when it is used in trees and cover represents the average coverage of the feature where coverage is defined as the number of samples affected by the split.
In conclusion, feature importance is a measure of how much each feature contributes to the model’s predictions, it helps us to understand which features are most important for our model. XGBoost provides an easy way to calculate feature importance using the built-in feature_importances_ attribute and also a plot_importance() function to visualize it. This visualization makes it easy to see which features are most important at a glance, and it can help you make informed decisions about which features to keep or remove from your dataset.
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