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Bagging Regressor | Commonly Used Bagging Models
Ensemble Learning

Bagging Regressor

Bagging Regressor creates an ensemble of multiple base regression models and combines their predictions to produce a final prediction. In Bagging Regressor, the base model is typically a regression algorithm, such as Decision Tree Regressor. The main idea behind Bagging Regressor is to reduce overfitting and improve the stability and accuracy of the predictions by averaging the predictions of multiple base models.

How does Bagging Regressor work?

  1. Bootstrap Sampling: The Bagging Regressor generates multiple subsets of the training data by randomly selecting samples with replacements. Each subset is called a bootstrap sample;
  2. Base Model Training: A separate base regression model (e.g., Decision Tree Regressor) is trained on each bootstrap sample. This creates multiple base models, each with its own variation due to the different subsets of data they were trained on;
  3. Aggregation of Predictions: The Bagging Regressor aggregates the predictions from all base models to make predictions. In the case of regression tasks, the predictions are typically averaged across the base models to form the final prediction. This ensemble approach helps to reduce overfitting and improve the overall model performance.

    Note

    Selecting samples with replacement is a concept often used in statistics and probability. It refers to a method of sampling data points or elements from a dataset or population where, after each selection, the selected item is put back into the dataset before the next selection. In other words, the same item can be chosen more than once in the sampling process.

Example of usage

The principle of using a Bagging Regressor in Python is the same as using a Bagging Classifier. The only difference is that Bagging Regressor has no implementation for the .predict_proba() method - the .predict() method is used to create predictions instead.

Note

If we don't specify the base model of AdaBoostRegressor, the DecisionTreeRegressor will be used by default.

Code Description
In the provided code, we perform the following steps:

  • Load the California Housing Dataset:
  • We use fetch_california_housing from sklearn.datasetsto load the California Housing dataset. This dataset contains features related to various aspects of housing in California, and the target variable is the median house value for California districts.
  • Split Data into Training and Testing Sets:
  • We split the dataset into training and testing sets using train_test_split() function from sklearn.model_selection module. The training set will be used to train the Bagging Regressor, and the testing set will be used to evaluate its performance.
  • Create Base Model:
  • We create a base model using DecisionTreeRegressor class from sklearn.tree module. The base model is a Decision Tree Regressor, which will be used as the weak learner within the Bagging Regressor.
  • Create Bagging Regressor:
  • We create an instance of BaggingRegressor class with the Decision Tree Regressor as the base model. The BaggingRegressor will train multiple instances of the base model on different subsets of the training data.
  • Train the Bagging Regressor:
  • We train the Bagging Regressor on the training data using the .fit() method. The Bagging Regressor sequentially trains multiple instances of the base model on different subsets of the training data to create the ensemble.
  • Make Predictions on the Test Data:
  • After training the Bagging Regressor, we use it to make predictions on the test data with the .predict() method. The Bagging Regressor combines the predictions of all base models to make the final predictions.
  • Evaluate Performance using Mean Squared Error (MSE):
  • We calculate the mean squared error (MSE) between the predicted median house values and the actual target values using mean_squared_error() function from sklearn.metrics module. MSE is a common metric used to evaluate regression models, and it gives us a measure of how well the Bagging Regressor is performing on the California Housing dataset.
    You can find the official documentation with all the necessary information about implementing this model in Python on the official website. Go here if needed.

    How does BaggingRegressor aggregates predictions of base models?

    Select the correct answer

    Everything was clear?

    Section 2. Chapter 3
    course content

    Course Content

    Ensemble Learning

    Bagging Regressor

    Bagging Regressor creates an ensemble of multiple base regression models and combines their predictions to produce a final prediction. In Bagging Regressor, the base model is typically a regression algorithm, such as Decision Tree Regressor. The main idea behind Bagging Regressor is to reduce overfitting and improve the stability and accuracy of the predictions by averaging the predictions of multiple base models.

    How does Bagging Regressor work?

    1. Bootstrap Sampling: The Bagging Regressor generates multiple subsets of the training data by randomly selecting samples with replacements. Each subset is called a bootstrap sample;
    2. Base Model Training: A separate base regression model (e.g., Decision Tree Regressor) is trained on each bootstrap sample. This creates multiple base models, each with its own variation due to the different subsets of data they were trained on;
    3. Aggregation of Predictions: The Bagging Regressor aggregates the predictions from all base models to make predictions. In the case of regression tasks, the predictions are typically averaged across the base models to form the final prediction. This ensemble approach helps to reduce overfitting and improve the overall model performance.

      Note

      Selecting samples with replacement is a concept often used in statistics and probability. It refers to a method of sampling data points or elements from a dataset or population where, after each selection, the selected item is put back into the dataset before the next selection. In other words, the same item can be chosen more than once in the sampling process.

    Example of usage

    The principle of using a Bagging Regressor in Python is the same as using a Bagging Classifier. The only difference is that Bagging Regressor has no implementation for the .predict_proba() method - the .predict() method is used to create predictions instead.

    Note

    If we don't specify the base model of AdaBoostRegressor, the DecisionTreeRegressor will be used by default.

    Code Description
    In the provided code, we perform the following steps:

  • Load the California Housing Dataset:
  • We use fetch_california_housing from sklearn.datasetsto load the California Housing dataset. This dataset contains features related to various aspects of housing in California, and the target variable is the median house value for California districts.
  • Split Data into Training and Testing Sets:
  • We split the dataset into training and testing sets using train_test_split() function from sklearn.model_selection module. The training set will be used to train the Bagging Regressor, and the testing set will be used to evaluate its performance.
  • Create Base Model:
  • We create a base model using DecisionTreeRegressor class from sklearn.tree module. The base model is a Decision Tree Regressor, which will be used as the weak learner within the Bagging Regressor.
  • Create Bagging Regressor:
  • We create an instance of BaggingRegressor class with the Decision Tree Regressor as the base model. The BaggingRegressor will train multiple instances of the base model on different subsets of the training data.
  • Train the Bagging Regressor:
  • We train the Bagging Regressor on the training data using the .fit() method. The Bagging Regressor sequentially trains multiple instances of the base model on different subsets of the training data to create the ensemble.
  • Make Predictions on the Test Data:
  • After training the Bagging Regressor, we use it to make predictions on the test data with the .predict() method. The Bagging Regressor combines the predictions of all base models to make the final predictions.
  • Evaluate Performance using Mean Squared Error (MSE):
  • We calculate the mean squared error (MSE) between the predicted median house values and the actual target values using mean_squared_error() function from sklearn.metrics module. MSE is a common metric used to evaluate regression models, and it gives us a measure of how well the Bagging Regressor is performing on the California Housing dataset.
    You can find the official documentation with all the necessary information about implementing this model in Python on the official website. Go here if needed.

    How does BaggingRegressor aggregates predictions of base models?

    Select the correct answer

    Everything was clear?

    Section 2. Chapter 3
    some-alt