Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.Challenge: Determining Feature Importances Using Random Forest | Commonly Used Bagging Models
Ensemble Learning

Challenge: Determining Feature Importances Using Random ForestChallenge: 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.

Завдання

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.

Все було зрозуміло?

Секція 2. Розділ 6
toggle bottom row
course content

Зміст курсу

Ensemble Learning

1. Basic Principles of Building Ensemble Models

What is Ensemble of Models?Bagging ModelsBoosting ModelsStacking Models

2. Commonly Used Bagging Models

Bagging ClassifierChallenge: Solving Task Using Bagging ClassifierBagging RegressorChallenge: Solving Task Using Bagging RegressorRandom ForestChallenge: Determining Feature Importances Using Random ForestExtraTrees

3. Commonly Used Boosting Models

AdaBoost ClassifierChallenge: Solving Task Using AdaBoost ClassifierChallenge: Solving Task Using AdaBoost RegressorGradient BoostingXGBoostChallenge: Solving Task Using XGBoost

4. Commonly Used Stacking Models

Stacking ClassifierChallenge: Solving Task Using Stacking ClassifierChallenge: Solving Task Using Stacking RegressorUsing Ensembles As Base ModelsCourse Summary

Ensemble Learning

Challenge: Determining Feature Importances Using Random ForestChallenge: 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.

Завдання

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.

Все було зрозуміло?

Секція 2. Розділ 6
toggle bottom row
some-alt