Challenge: Solving Task Using Bagging Regressor
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The load_diabetes dataset contains ten baseline variables (age, sex, BMI, average blood pressure, and six blood serum measurements) for 442 diabetes patients. The target variable is a quantitative measure of disease progression one year after baseline. This dataset is used for predicting the continuous variable, representing diabetes progression, based on the given features.
Your task is to use Bagging Regressor to solve the regression problem on load_diabetes dataset:
- Use a simple
LinearRegressionmodel as the base model of the ensemble. - Use the
BaggingRegressorclass to create an ensemble. - Use Mean Squared Error(MSE) to evaluate the results.
Lösning
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Completion rate improved to 4.55Challenge: Solving Task Using Bagging Regressor
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Swipe to start coding
The load_diabetes dataset contains ten baseline variables (age, sex, BMI, average blood pressure, and six blood serum measurements) for 442 diabetes patients. The target variable is a quantitative measure of disease progression one year after baseline. This dataset is used for predicting the continuous variable, representing diabetes progression, based on the given features.
Your task is to use Bagging Regressor to solve the regression problem on load_diabetes dataset:
- Use a simple
LinearRegressionmodel as the base model of the ensemble. - Use the
BaggingRegressorclass to create an ensemble. - Use Mean Squared Error(MSE) to evaluate the results.
Lösning
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Completion rate improved to 4.55single
