Challenge: Solving Task Using AdaBoost Regressor
AdaBoost Regressor is an ensemble learning algorithm used for regression tasks.
The principle of work of such a regressor coincides with the principle of work of the AdaBoost Classifier. The only difference is that we use some regression algorithms (linear regression, decision tree regressor, polynomial regression, etc.) as a base model.
The AdaBoostRegressor class in Python provides tools to train the model and make predictions.
Завдання
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Your task is to create a model to solve the regression task on the diabetes dataset:
- Use a simple Linear Regression model as the base model of an ensemble.
- Create an AdaBoost Regressor model with the 50 base estimators.
- Print MSE to estimate regression quality.
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import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import load_diabetes
from sklearn.model_selection import train_test_split
from sklearn.ensemble import AdaBoostRegressor
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
# Load the Wine Quality dataset
data = load_diabetes()
X = data.data
y = data.target
# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
# Create and train the AdaBoost Regressor with Linear Regression base model
base_model = LinearRegression()
regr = AdaBoostRegressor(base_model, n_estimators=50)
regr.fit(X_train, y_train)
# Make predictions
y_pred = regr.predict(X)
# Calculate Mean Squared Error
mse = mean_squared_error(y, y_pred)
print(f'MSE: {mse:.4f}')
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Секція 3. Розділ 3
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import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import load_diabetes
from sklearn.model_selection import train_test_split
from sklearn.ensemble import AdaBoostRegressor
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
# Load the Wine Quality dataset
data = load_diabetes()
X = data.data
y = data.target
# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
# Create and train the AdaBoost Regressor with Linear Regression base model
base_model = ___()
regr = ___(___, n_estimators=___)
regr.fit(X_train, y_train)
# Make predictions
y_pred = regr.predict(X)
# Calculate Mean Squared Error
mse = mean_squared_error(y, y_pred)
print(f'MSE: {___:.4f}')
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