Random Forest can determine **feature importances**, and it's one of its useful features. Feature importance is a measure that quantifies the **contribution of each feature** in the dataset to the predictive performance of the Random Forest model.   
To determine feature importance, we must conduct the following steps:
1. Train the Random Forest model on the necessary dataset.
2. Use the `.feature_importances_` attribute of the trained model to get the importance values of all features.
This attribute returns an array of values, each corresponding to the importance of a specific feature in the dataset. The values are normalized and sum up to 1, making comparing the relative importance of different features easier.

Ensemble Learning is an advanced machine learning technique that combines multiple models to improve overall predictive performance and decision-making when solving real-life tasks.

What is an ensemble? How are ensembles different from standard machine-learning models? What are the types of ensembles? Let's consider the answers to these questions.

Let's consider some commonly used bagging ensemble models, the features of their use, and also apply some of them to solve real-life tasks.

The mechanism of work of boosting models differs from bagging models. Now we will explore these distinctions, gain insights into utilizing model boosting for problem-solving, and illustrate its functionality through practical demonstrations.

Let's consider some commonly used stacking ensemble models, the features of their use, and also apply some of them to solve real-life tasks.

Challenge: Determining Feature Importances Using Random Forest

Solution

Awesome!

Challenge: Determining Feature Importances Using Random Forest

Solution

Awesome!