(Regression) Load the Boston dataset from sklearn. Split the dataset into training and testing parts. Use ridge regression (linear_model.Ridge) to predict the target values. Try with both the original attributes and polynomial features (preprocessing.PolynomialFeatures(2,interaction_only=True)). Determine the best regularization coefficient (alpha) in each case. Plot true value vs. predicted values. Finally, decide on a single attribute, try to predict the values using a single attribute.
(Regression) Load the Boston dataset from sklearn. Split the dataset into training and testing parts. Use ridge regression (linear_model.Ridge) to predict the target values. Try with both the original attributes and polynomial features (preprocessing.PolynomialFeatures(2,interaction_only=True)). Determine the best regularization coefficient (alpha) in each case. Plot true value vs. predicted values. Finally, decide on a single attribute, try to predict the values using a single attribute.
Chapter6: Additional Database Objects
Section: Chapter Questions
Problem 5HOA
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(Regression) Load the Boston dataset from sklearn. Split the dataset into training and testing
parts. Use ridge regression (linear_model.Ridge) to predict the target values. Try with both the original
attributes and polynomial features (preprocessing.PolynomialFeatures(2,interaction_only=True)).
Determine the best regularization coefficient (alpha) in each case. Plot true value vs. predicted values.
Finally, decide on a single attribute, try to predict the values using a single attribute.
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