Challenge: Feature Selection Pipeline
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You will build a feature selection + regression pipeline to predict disease progression using the Diabetes dataset. Your goal is to combine preprocessing, feature selection, and model training in one efficient workflow.
Follow these steps:
- Load the dataset using
load_diabetes(). - Split it into train/test sets (
test_size=0.3,random_state=42). - Build a pipeline with:
StandardScaler().SelectFromModel(Lasso(alpha=0.01, random_state=42))for automatic feature selection.LinearRegression()as the final model.
- Fit the pipeline and evaluate it using R² score on the test set.
- Print:
- The R² score (rounded to 3 decimals).
- The number of features selected.
Lösning
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Completion rate improved to 8.33Challenge: Feature Selection Pipeline
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Swipe to start coding
You will build a feature selection + regression pipeline to predict disease progression using the Diabetes dataset. Your goal is to combine preprocessing, feature selection, and model training in one efficient workflow.
Follow these steps:
- Load the dataset using
load_diabetes(). - Split it into train/test sets (
test_size=0.3,random_state=42). - Build a pipeline with:
StandardScaler().SelectFromModel(Lasso(alpha=0.01, random_state=42))for automatic feature selection.LinearRegression()as the final model.
- Fit the pipeline and evaluate it using R² score on the test set.
- Print:
- The R² score (rounded to 3 decimals).
- The number of features selected.
Lösning
Byt till skrivbordet för praktisk övningFortsätt där du är med ett av alternativen nedanTack för dina kommentarer!
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