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Challenge: Determining Feature Importances Using Random Forest | Commonly Used Bagging Models
Ensemble Learning

Challenge: Determining Feature Importances Using Random Forest

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.

Task

The 'heart_disease' dataset is a commonly used dataset for binary classification tasks in machine learning. It contains various medical attributes related to patients' health and aims to predict the presence or absence of heart disease in an individual.

Your task is to determine the importance of features of the heart disease dataset:

  1. Use RandomForestClassifier class to create a model.
  2. Fit the classifier on the dataset.
  3. Find the importance of all features.

Everything was clear?

Section 2. Chapter 6
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course content

Course Content

Ensemble Learning

Challenge: Determining Feature Importances Using Random Forest

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.

Task

The 'heart_disease' dataset is a commonly used dataset for binary classification tasks in machine learning. It contains various medical attributes related to patients' health and aims to predict the presence or absence of heart disease in an individual.

Your task is to determine the importance of features of the heart disease dataset:

  1. Use RandomForestClassifier class to create a model.
  2. Fit the classifier on the dataset.
  3. Find the importance of all features.

Everything was clear?

Section 2. Chapter 6
toggle bottom row
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