Course Content
Ensemble Learning
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:
- Train the Random Forest model on the necessary dataset.
- 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.
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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:
- Use
RandomForestClassifierclass to create a model. - Fit the classifier on the dataset.
- Find the importance of all features.
Switch to desktop for real-world practiceContinue from where you are using one of the options belowThanks for your feedback!
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:
- Train the Random Forest model on the necessary dataset.
- 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.
Swipe to show code editor
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:
- Use
RandomForestClassifierclass to create a model. - Fit the classifier on the dataset.
- Find the importance of all features.
Switch to desktop for real-world practiceContinue from where you are using one of the options belowThanks for your feedback!
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:
- Train the Random Forest model on the necessary dataset.
- 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.
Swipe to show code editor
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:
- Use
RandomForestClassifierclass to create a model. - Fit the classifier on the dataset.
- Find the importance of all features.
Switch to desktop for real-world practiceContinue from where you are using one of the options belowThanks for your feedback!
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:
- Train the Random Forest model on the necessary dataset.
- 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.
Swipe to show code editor
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:
- Use
RandomForestClassifierclass to create a model. - Fit the classifier on the dataset.
- Find the importance of all features.
Switch to desktop for real-world practiceContinue from where you are using one of the options belowSwitch to desktop for real-world practiceContinue from where you are using one of the options below