Зміст курсу
Ensemble Learning
Ensemble Learning
Challenge: Solving Task Using Bagging Regressor
Завдання
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
LinearRegression
model as the base model of the ensemble. - Use the
BaggingRegressor
class to create an ensemble. - Use Mean Squared Error(MSE) to evaluate the results.
Дякуємо за ваш відгук!
Challenge: Solving Task Using Bagging Regressor
Завдання
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
LinearRegression
model as the base model of the ensemble. - Use the
BaggingRegressor
class to create an ensemble. - Use Mean Squared Error(MSE) to evaluate the results.
Дякуємо за ваш відгук!
Challenge: Solving Task Using Bagging Regressor
Завдання
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
LinearRegression
model as the base model of the ensemble. - Use the
BaggingRegressor
class to create an ensemble. - Use Mean Squared Error(MSE) to evaluate the results.
Дякуємо за ваш відгук!
Завдання
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
LinearRegression
model as the base model of the ensemble. - Use the
BaggingRegressor
class to create an ensemble. - Use Mean Squared Error(MSE) to evaluate the results.