Boost Your Machine Learning Models with Bagging: A Powerful Ensemble Learning Technique

8 min readApr 25, 2023

Introduction

Machine learning models have evolved into indispensable tools for tackling complicated issues in various disciplines. On the other hand, building extraordinarily accurate and robust models can be difficult, even with advances in algorithms and methodologies. To address this issue, ensemble learning approaches such as bagging have grown in favor. The process of merging the predictions of numerous base models to produce a more accurate and resilient model is known as ensemble learning. The goal is to use the diversity of the base models to improve overall prediction.

Bagging is an acronym for Bootstrap Aggregating, and it is a potent ensemble learning strategy that has been shown to improve model performance. Bagging can be used to solve a variety of machine-learning issues, such as classification, regression, and clustering. In this blog article, we will look in depth at bagging and how it can be used to improve the accuracy and robustness of machine learning models.

Bagging Explained

Bagging is a technique in which numerous instances of the same base model are trained on distinct portions of the training data. The subsets are generated via bootstrapping, which involves randomly selecting the training data with replacement. Each base model is trained on its own bootstrapped subset of data. The underlying models’ forecasts are then integrated using a voting or averaging procedure to produce the final prediction.

What Effect Does Bagging Have on Model Performance?

Bagging can help machine learning models perform better in numerous ways:

Bagging decreases the possibility of overfitting by training base models on distinct subsets of the training data. Each base model learns from a slightly different angle of the data, which aids in generalization and reduces overfitting.
Prediction Accuracy Improvement: Bagging can increase a model’s prediction accuracy by lowering prediction variance. The combined forecasts of the base models are more stable and less susceptible to outliers or noise in the data, yielding a more accurate prediction.
Outlier and Noise resilience: Bagging can improve model resilience by decreasing the influence of outliers and noisy data points. Because each base model is trained on a separate sample of the data, the impact of single outliers or noisy points is reduced, resulting in a more robust prediction.
Handling Imbalanced Data: Bagging can also be used to handle data that has an uneven distribution of classes. Bagging can assist capture minority class instances more effectively and improve prediction accuracy for imbalanced datasets by training base models on diverse subsets of the data.

The Relationship Between Bagging and Ensemble Learning

Bagging is a form of ensemble learning technique in which the predictions of numerous base models are combined to generate a more accurate and robust model. It is a prominent strategy in ensemble learning and can be combined with other ensemble strategies like boosting and stacking to increase model performance even further. Bagging is well-known for its ease of use and efficacy, and it has been utilized in a variety of machine-learning algorithms, including decision trees, random forests, and support vector machines, to mention a few.

Using Bagging in Machine Learning

The following steps are involved in implementing bagging in machine learning:

Select a Base Model: Select a base model that is known to perform well on a particular task. The base model should have a high variance, which indicates that it overfits the data.
Bootstrapped Sampling: Using random sampling with replacement, create numerous subsets of the training data. The ensemble’s desired number of base models is equal to the number of subgroups formed. Each subset should be the same size as the original training data, and each data point should have an equal chance of inclusion in each subset. This is known as bootstrapped sampling, and it aids in the creation of variety among base models.
Base Model Training: Independently train each base model on its bootstrapped subset of training data. Fitting the base model to the subset of data and deriving the model parameters or weights is what this entails.
Combination of Prediction Models: Once all of the base models have been trained, they may be utilized to make predictions on the test data. To create the final prediction, the predictions of the base models can be integrated using a voting or averaging approach. The most popular strategy for classification issues is to employ majority voting, in which the class with the most votes from the base models is chosen as the final prediction. The predictions for regression issues can be averaged to give the final forecast.
Evaluation and Performance Analysis: Depending on the kind of problem, evaluate the performance of the bagged ensemble model using appropriate evaluation metrics such as accuracy, precision, recall, F1-score, or RMSE. To evaluate the improvement in model performance, compare the performance of the bagged ensemble model to the performance of the base model.
To further optimize model performance, fine-tune the hyperparameters of the basic model and the bagging ensemble. Experiment with different hyperparameter values to identify the optimum combination for the given problem, such as the number of base models in the ensemble, the sample ratio for bootstrapped sampling, and the type of prediction combination scheme.

Simple way👇

Benefits of Bagging

In machine learning, bagging has various advantages:

Improved Prediction Accuracy: By reducing variance and overfitting, bagging can improve the prediction accuracy of the base model, resulting in a more accurate and stable forecast.
Bagging can improve the model’s robustness to noise and outliers by lowering the influence of noisy data points or outliers, resulting in a more trustworthy prediction.
Handling Imbalanced Data: By collecting minority class instances more efficiently and enhancing prediction accuracy for imbalanced issues, bagging may effectively manage skewed datasets.
Bagging is a scalable approach that may be applied to huge datasets and parallelized to accelerate training.
Model Diversity: Through bootstrapped sampling, bagging promotes diversity among the base models, which can increase overall prediction performance by utilizing the strengths of diverse models.

Using a Real-World Example to Implement Bagging in Machine Learning

Example: Using Bagging to Predict Breast Cancer Diagnosis

Intro:

A breast cancer diagnosis is an important part of medical diagnostics. Consider a dataset of breast cancer patients with characteristics like age, tumor size, tumor form, margin, and so on, as well as binary labels indicating whether the tumor is malignant (1) or benign (0).

Steps:

Base Model Selection: We can start with a base model like a decision tree or a random forest classifier, which are known to perform well on classification tasks and have a high variance, making them prone to overfitting.
Bootstrapped Sampling: Using bootstrapped sampling, we produce multiple subsets of the training data. Assume we make ten subsets (or “bags”) of the training data, each with a random sampling of 80% of the original data and replacement. This means that some samples will be repeated in each bag, while others will be skipped.
Training the Base Model: We train the base model (e.g., a decision tree) independently on each bag of training data. Fitting the decision tree to each bag and obtaining the model parameters or weights is what this entails.
Combination of Prediction Models: Once all of the base models have been trained, we may use them to make predictions on the test data. The underlying models’ forecasts can be blended using majority voting. For example, if six out of ten base models predict a tumor to be malignant, we can take the majority vote and declare the tumor to be malignant.
Evaluation and Performance Analysis: We assess the bagged ensemble model’s performance using appropriate evaluation metrics such as accuracy, precision, recall, and F1-score. To assess the improvement in model performance, we compare the performance of the bagged ensemble model to the performance of the base model.
Hyperparameter Tuning: To improve model performance, we can fine-tune the hyperparameters of the basic model and the bagging ensemble. To discover the optimum combination for a specific problem, we can experiment with alternative hyperparameter values, such as the number of base models in the ensemble, the sampling ratio for bootstrapped sampling, and the type of prediction combination method.

Conclusion for this example:

In this example, we implemented bagging in machine learning for breast cancer diagnosis using a decision tree as the base model. The bagged ensemble of decision trees can help improve the accuracy, robustness, and reliability of the prediction model. By leveraging the strengths of multiple base models through bootstrapped sampling and prediction combination, bagging can provide more accurate and stable predictions compared to using a single base model. It is important to fine-tune the hyperparameters of the base model and the bagging ensemble to achieve the best performance. Bagging is a powerful ensemble learning technique that can be applied to various machine learning algorithms and has practical applications in real-life problems.

Code formate

# Import necessary libraries
from sklearn.ensemble import BaggingClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# Load breast cancer dataset
data = load_breast_cancer()
X = data.data
y = data.target

# Split the data into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Base model selection: Decision Tree Classifier
base_model = DecisionTreeClassifier(random_state=42)

# Create a BaggingClassifier with 10 base models
bagging_model = BaggingClassifier(base_model, n_estimators=10, random_state=42)

# Fit the bagging model to the training data
bagging_model.fit(X_train, y_train)

# Make predictions on the test data
y_pred = bagging_model.predict(X_test)

# Calculate accuracy of the bagging model
accuracy = accuracy_score(y_test, y_pred)

print("Accuracy of Bagging Classifier: {:.2f}".format(accuracy))

Conclusion

Bagging and other ensemble learning techniques are effective tools for enhancing the accuracy and durability of machine learning models. Bagging can help to reduce overfitting, enhance prediction accuracy, handle imbalanced data, and make the model more resilient to noise and outliers. It is a scalable strategy that is simple to incorporate into many machine learning algorithms.

Bagging can dramatically improve the performance of machine learning models by selecting a wide number of base models, doing bootstrapped sampling, training the base models independently, and aggregating their predictions. Incorporating bagging into your workflow as a machine learning practitioner can be a beneficial strategy for improving the accuracy and dependability of your models.