Challenge: Solving Task Using Bagging Regressor
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.
Soluzione
Grazie per i tuoi commenti!
single
Chieda ad AI
Chieda ad AI
Chieda pure quello che desidera o provi una delle domande suggerite per iniziare la nostra conversazione
Awesome!
Completion rate improved to 4.55Challenge: Solving Task Using Bagging Regressor
Scorri per mostrare il menu
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.
Soluzione
Cambia al desktop per esercitarti nel mondo realeContinua da dove ti trovi utilizzando una delle opzioni seguentiGrazie per i tuoi commenti!
Awesome!
Completion rate improved to 4.55single
