AdaBoost Algorithm in Machine Learning in 2025

The AdaBoost (Adaptive Boosting) algorithm is a popular ensemble learning technique in machine learning that combines multiple weak learners to create a stronger, more accurate model. It works by iteratively training weak classifiers, typically decision stumps (simple decision trees), and adjusting their weights based on their performance. Initially, all training data points are given equal weight. After each weak learner is trained, the algorithm focuses more on the misclassified data points by increasing their weight while reducing the weights of correctly classified points. 

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This ensures that the subsequent weak learners focus on the hardest examples, gradually improving the model's performance. The final model is a weighted combination of all the weak learners, where each classifier contributes based on its accuracy. AdaBoost minimizes both bias and variance by emphasizing difficult cases and improving overall model robustness. 

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Although AdaBoost can significantly improve accuracy, it is sensitive to noisy data and outliers since incorrect predictions are heavily weighted during training. It has been widely used in various applications such as image classification, fraud detection, and sentiment analysis. AdaBoost's ability to boost weak models without requiring complex classifiers makes it a powerful tool in machine learning.

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What is AdaBoost?

AdaBoost (Adaptive Boosting) is an ensemble machine learning algorithm that combines multiple weak learners to create a strong learner. A weak learner is a model that performs slightly better than random guessing, typically a simple model like a decision stump (a decision tree with a single split). AdaBoost works by iteratively training these weak learners, adjusting the weights of misclassified data points after each round to focus on the examples that are hardest to classify.The key idea behind AdaBoost is that it "boosts" the performance of weak models by giving more importance to difficult-to-classify examples. Initially, all training instances are given equal weights.

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After each weak learner is trained, AdaBoost increases the weights of the misclassified data points, forcing the next learner to focus more on these examples. In contrast, the weights of correctly classified points are decreased. After several iterations, the weak models are combined into a final strong model by taking a weighted vote of their predictions, with more accurate models receiving more influence.AdaBoost is effective in reducing both bias and variance, making it a powerful tool for improving model accuracy, especially when using weak base classifiers. However, it can be sensitive to noisy data and outliers, as they tend to be given higher weights, potentially leading to overfitting.

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Python implementation of AdaBoost

To implement AdaBoost in Python, we can use the scikit-learn library, which provides an easy-to-use implementation of AdaBoost through the AdaBoostClassifier class. Below is a simple example demonstrating how to implement AdaBoost for classification tasks.

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Python Implementation of AdaBoost

1. Install necessary libraries (if you haven't already)

You can install sci-kit-learn using pip if you don't have it installed:

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Pipip install sci-kit-learn

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2. Example Code:

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# Import necessary libraries
import numpy asFromfrom sklearn.model_selection import train_test_split
From sklearn.ensemble import AdaBoostClassifiFromrom sklearn.Tree import DecisionTreeClassifiFromrom learn.datasets import make_classificatiFromrom sklearn.metrics import accuracy_score

# Generate a synthetic dataset
X, y = make_classification(n_samples=1000, n_features=20, n_informative=10, n_classes=2, random_state=42)

# Split dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Create a weak learner (Decision Tree Classifier with max depth 1)
weak_learner = DecisionTreeClassifier(max_depth=1)

# Initialize the AdaBoost classifier with the weak learner
ada_boost = AdaBoostClassifier(base_estimator=weak_learner, n_estimators=50, random_state=42)

# Train the AdaBoost model
ada_boost.fit(X_train, y_train)

# Predict on the test set
y_pred = ada_boost.predict(X_test)

# Evaluate the model
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy of AdaBoost Classifier: {accuracy * 100:.2f}%")

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How AdaBoost Works

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AdaBoost (Adaptive Boosting) is a powerful ensemble learning algorithm that improves the performance of weak classifiers by combining them into a strong classifier. Here’s how it works step-by-step:

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1. Initialize Weights for Training Data

• AdaBoost starts by assigning equal weights to all training instances. In the beginning, each data point has the same importance (weight = 1/total number of samples).

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2. Train the First Weak Learner

• The algorithm trains a weak classifier (typically a simple model like a decision stump, which is a one-level decision tree) on the dataset. This classifier makes predictions based on the available features.

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3. Calculate the Error Rate

• After training the weak learner, AdaBoost calculates the error rate of the model, which is the sum of the weights of the misclassified data points.

• The error is calculated as: Error=∑Weight of Misclassified Points∑Total Weight\text{Error} = \frac{\sum \text{Weight of Misclassified Points}}{\sum \text{Total Weight}}Error=∑Total Weight∑Weight of Misclassified Points​

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4. Update Weights for Misclassified Points

• AdaBoost increases the weights of the misclassified data points. This is because the algorithm aims to focus more on the instances that the current classifier failed to predict correctly.

• The weight for each misclassified point is updated by multiplying it by a factor determined by the error rate of the weak learner: New Weight=Old Weight×(11−Error Rate)\text{New Weight} = \text{Old Weight} \times \left( \frac{1}{1 - \text{Error Rate}} \right)New Weight=Old Weight×(1−Error Rate1​)

• Correctly classified data points have their weights decreased, making them less significant for future learners.

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5. Train the Next Weak Learner

• A new weak learner is trained on the updated weighted dataset, focusing more on the previously misclassified instances.

• The same process is repeated for a fixed number of iterations (n_estimators) or until the error rate reaches a satisfactory level.

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6. Combine the Weak Learners

• After several rounds of training, AdaBoost combines all the weak classifiers into a strong classifier by taking a weighted vote based on each classifier’s accuracy. Classifiers that perform better are given higher weights.

• The final prediction is made by aggregating the predictions of all weak learners, where each learner contributes according to its accuracy: Final Prediction=∑(Weight of Learner×Prediction of Learner)\text{Final Prediction} = \sum (\text{Weight of Learner} \times \text{Prediction of Learner})Final Prediction=∑(Weight of Learner×Prediction of Learner)

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7. Final Strong Model

• The final model is a weighted combination of all the weak learners. The model is stronger than any individual learner because it focuses on improving the hardest cases that previous models misclassified.

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Mathematical Foundation

The mathematical foundation of AdaBoost (Adaptive Boosting) revolves around the concept of combining multiple weak learners into a single strong classifier.

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It focuses on minimizing the weighted classification error by iteratively adjusting the weights of training data points, emphasizing the misclassified examples. Below is the mathematical formulation that underpins AdaBoost:

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1. Weights Initialization

Initially, all training examples have the same weight, which is given by:Where:

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• D1(i)D_1(i)D1​(i) is the weight of the ithi^{th}ith training sample.

• NNN is the total number of training examples.

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2. Training a Weak Learner

A weak learner (e.g., a decision stump) is trained on the dataset using the weighted data. The weak learner tries to minimize the weighted classification error, which is given by:Where:

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• ϵt\epsilon_tϵt​ is the weighted error rate of the weak classifier at iteration it.

• MtM_tMt​ represents the set of misclassified data points during round tit

• Dt(i)D_t(i)Dt​(i) is the weight of the ithi^{th}ith sample in round ttit3. Calculating the Weak Learner’s Weight

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Once the weak learner's error rate ϵt\epsilon_tϵt​ is computed, the weight of the weak learner is determined based on its error rate. If the weak learner performs well (low error), it gets a higher weight, and vice versa. The weight is computed as:

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αt=12ln⁡(1−ϵtϵt)\alpha_t = \frac{1}{2} \ln\left(\frac{1 - \epsilon_t}{\epsilon_t}\right)αt​=21​ln(ϵt​1−ϵt​​)

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Where:

• αt\alpha_tαt​ is the weight assigned to the weak learner at iteration it.

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4. Update the Weights of Training Data

After each weak learner is trained, AdaBoost updates the weights of the training instances. Misclassified points are given more weight, while correctly classified points are given less weight. The updated weight for each training example iii is given by:

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Dt+1(i)=Dt(i)⋅exp⁡(−αt⋅yi⋅ht(xi))D_{t+1}(i) = D_t(i) \cdot \exp\left(-\alpha_t \cdot y_i \cdot h_t(x_i)\right)Dt+1​(i)=Dt​(i)⋅exp(−αt​⋅yi​⋅ht​(xi​))

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where:

• yiy_iyi​ is the true label of the ithi^{th}ith sample.

• ht(xi)h_t(x_i)ht​(xi​) is the prediction of the weak learner at iteration it for the ithi^{th}ith data point.

• αt\alpha_tαt​ is the weight of the weak learner as calculated above.

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The weight normalization is performed to ensure that the sum of the weights of all training examples remains 1:

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Dt+1(i)=Dt+1(i)∑i=1NDt+1(i)D_{t+1}(i) = \frac{D_{t+1}(i)}{\sum_{i=1}^{N} D_{t+1}(i)}Dt+1​(i)=∑i=1N​Dt+1​(i)Dt+1​(i)​

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5. Final Classifier

After TTT rounds of boosting, the final strong classifier is obtained by combining the weak learners. The final prediction is made by a weighted majority vote of all the weak classifiers:

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H(x)=sign(∑t=1Tαtht(x))H(x) = \text{sign}\left(\sum_{t=1}^{T} \alpha_t h_t(x)\right)H(x)=sign(t=1∑T​αt​ht​(x))

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where:

• H(x)H(x)H(x) is the final prediction for input xxx.

• ht(x)h_t(x)ht​(x) is the prediction of the weak classifier at iteration it.

• αt\alpha_tαt​ is the weight of the weak learner at iteration it.

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Key Characteristics of AdaBoost

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The AdaBoost (Adaptive Boosting) algorithm has several key characteristics that distinguish it from other machine learning techniques. These characteristics highlight why it is effective in improving the performance of weak learners and solving complex classification problems. Here are the key features:

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1. Ensemble Method

• AdaBoost is an ensemble learning technique, meaning it combines multiple models (weak learners) to form a stronger, more accurate model. It works by iteratively training weak classifiers and then combining them to improve overall prediction accuracy.

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2. Focus on Misclassified Points

• One of the core ideas of AdaBoost is to focus on the misclassified data points during each iteration. It adjusts the weights of these misclassified instances, forcing subsequent weak learners to focus more on those examples and improve classification accuracy.

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3. Weak Learners

• AdaBoost typically uses weak learners, such as decision stumps (one-level decision trees), which are models that perform slightly better than random guessing. Despite the simplicity of weak learners, AdaBoost combines many of them to create a strong classifier.

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4. Adaptive Nature

• The algorithm is adaptive because it adjusts the training process at each step based on the performance of previous models. The algorithm increases the weights of misclassified instances, adapting the training process to focus on more challenging examples.

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5. Weighted Voting

• The final prediction in AdaBoost is made using weighted voting from all the weak learners. Each weak learner contributes to the final prediction, with more accurate classifiers having a higher weight, so they have a larger influence on the final decision.

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6. Minimizes Bias and Variance

• AdaBoost helps to reduce both bias and variance. By iteratively focusing on hard-to-classify points, AdaBoost can reduce the bias of the weak learner. It also reduces variance by combining multiple weak models, which lowers the overall risk of overfitting.

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7. Sensitivity to Noise and Outliers

• One of the drawbacks of AdaBoost is that it is sensitive to noisy data and outliers. Since the algorithm increases the weight of misclassified instances, noisy data or outliers that are misclassified may need more focus, leading to overfitting.

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8. No Need for Feature Scaling

• AdaBoost does not require feature scaling (like normalization or standardization) since it focuses on the error rate of weak learners rather than the scale of individual features. This makes the algorithm more straightforward to implement.

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9. Can Handle Multiple Classes

• While AdaBoost is commonly used for binary classification, it can be extended to handle multi-class problems using techniques like the SAMME algorithm (Stagewise Additive Modeling using a Multi-class Exponential loss function).

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10. Improves Performance with More Iterations

• As the number of boosting rounds (iterations) increases, AdaBoost tends to improve the performance of the model, especially if there is enough data to justify the additional weak learners. However, after a certain point, increasing the number of iterations may lead to overfitting, especially if the model becomes too complex for the dataset.

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11. Works Well with Simple Models

• AdaBoost is highly effective when combined with simple classifiers that have weak predictive power on their own (e.g., decision trees with a single split or small decision trees). These weak learners are typically inexpensive to train, and AdaBoost amplifies their power by focusing on difficult cases.

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Advantages of AdaBoost

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The AdaBoost (Adaptive Boosting) algorithm offers several advantages that make it a popular and powerful tool for machine learning tasks, particularly in classification problems. Here are the key advantages of AdaBoost:

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1. Improved Accuracy

• Boosted Accuracy: AdaBoost significantly improves the accuracy of weak classifiers. By combining multiple weak learners (such as decision stumps), the algorithm creates a strong classifier that typically performs much better than any individual weak learner. This makes it a highly effective method for improving model performance.

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2. Simple and Efficient

• Simple to Implement: AdaBoost is relatively easy to implement compared to other ensemble methods. It works well with simple base classifiers (like decision trees) and does not require complex computations, which makes it both efficient and scalable.

• Low Computational Complexity: While AdaBoost does involve iterating over several weak learners, the computational complexity is still lower compared to other complex ensemble methods, like Random Forests or Gradient Boosting, especially when using simple base learners.

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3. No Need for Feature Scaling

• Works without Normalization/Standardization: Unlike some other machine learning algorithms that require feature scaling (e.g., SVMs or k-nearest neighbors), AdaBoost can handle raw, unscaled data. This simplifies the preprocessing steps and reduces the need for additional transformations like feature scaling or normalization.

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4. Versatility

• Works with Different Classifiers: While decision trees are the most common choice for base learners, AdaBoost can work with any classifier. This makes it versatile and applicable to a wide range of classification tasks.

• Multi-Class Classification: Although AdaBoost was originally designed for binary classification, it can be extended to handle multi-class classification problems by using algorithms like SAMME (Stagewise Additive Modeling using a Multi-class Exponential loss function).

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5. Reduces Overfitting

• Prevents Overfitting (in Most Cases): While overfitting is a common concern in machine learning, AdaBoost tends to resist overfitting, especially when using weak learners. By focusing on hard-to-classify examples and iterating over them, AdaBoost can reduce the variance of the model without increasing bias.

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Disadvantages of AdaBoost

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1. Sensitivity to Noisy Data and Outliers

• Impact of Noisy Data: AdaBoost assigns higher weights to misclassified instances, which can cause the algorithm to focus too much on noisy data or outliers. If there are mislabeled or erroneous data points, the algorithm may overfit those points, leading to poor generalization and reduced model accuracy.

• Outliers: AdaBoost can be highly sensitive to outliers, especially when the number of boosting rounds is large. Outliers that are misclassified will receive higher weights, which can disproportionately influence the final model.

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2. Overfitting with Too Many Rounds

• Risk of Overfitting: While AdaBoost generally resists overfitting, it can still overfit if the number of boosting iterations (rounds) is too high, especially when the weak learners are not simple enough or if there is a lot of noise in the dataset. If the model is trained too long, it may start to fit the noise in the data rather than the underlying patterns.

• Decreasing Returns: After a certain number of boosting rounds, the model’s performance may plateau or even decrease, indicating diminishing returns from further iterations.

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3. Computational Cost

• Higher Computational Cost: Although AdaBoost is relatively efficient compared to other ensemble methods, the iterative nature of boosting can still make it computationally expensive, especially with large datasets. Each weak learner requires training on the entire dataset, and as the number of iterations increases, the overall computational burden also rises.

• Slower Training Time: Training a large number of weak learners for a significant number of rounds can lead to slower training times, particularly when using complex base learners.

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4. Limited Flexibility with Base Learners

• Choice of Weak Learner: AdaBoost is typically used with simple, weak learners such as decision stumps (one-level decision trees). While it can theoretically work with other classifiers, its performance may degrade if the base learner is too complex or unsuitable for boosting. Complex learners may lead to overfitting, especially when used for a large number of iterations.

• Base Learner Quality: AdaBoost’s performance heavily depends on the base learner’s ability to perform slightly better than random guessing. If the base learner is too weak (or too strong), AdaBoost may not perform well.

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5. Difficult to Tune

• Hyperparameter Sensitivity: AdaBoost has a few hyperparameters that need to be tuned, such as the number of boosting rounds (iterations) and the choice of weak learners. Finding the optimal set of hyperparameters can be challenging and may require extensive cross-validation.

• No Simple Rules for Tuning: Unlike some other machine learning algorithms, there are no simple, intuitive rules for tuning AdaBoost’s parameters, making it a trial-and-error process.

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Applications of AdaBoost

The AdaBoost (Adaptive Boosting) algorithm is widely used in various domains due to its ability to improve weak models by focusing on hard-to-classify examples. Here are some of the key applications of AdaBoost:

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1. Image Classification

• AdaBoost is commonly used for image classification tasks, such as face detection. In the Viola-Jones face detection framework, AdaBoost combines weak classifiers to detect faces in images. Its ability to adapt and focus on difficult-to-classify areas of an image makes it particularly effective in this domain.

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2. Spam Email Detection

• AdaBoost is often employed in spam email detection. It helps classify emails as spam or not by improving weak classifiers that can differentiate spam emails from legitimate ones. AdaBoost's iterative correction of misclassified instances helps enhance accuracy in filtering out unwanted emails.

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3. Medical Diagnosis

• AdaBoost has applications in medical diagnostics to detect diseases or analyze medical images. For example, it can be used in cancer detection, where weak classifiers combine to improve the detection of tumors or abnormal tissue in radiology images, improving early diagnosis accuracy.

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4. Financial Fraud Detection

• AdaBoost is widely applied in the financial industry, particularly for fraud detection in credit card transactions, insurance claims, and banking. By focusing on difficult cases, such as subtle fraudulent activities, it helps in detecting unusual patterns that indicate fraud.

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5. Customer Segmentation

• In marketing, AdaBoost can be used for customer segmentation. By classifying customers based on their purchasing behavior or demographic data, businesses can better target specific groups for promotions, improving customer engagement and sales.

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AdaBoost vs Other Ensemble Methods

Ensemble methods combine multiple models to improve prediction accuracy. AdaBoost, a boosting algorithm, focuses on correcting the errors of previous models. Other popular ensemble methods include Bagging (Random Forest), Gradient Boosting, and XGBoost, each with its unique characteristics and strengths.

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Conclusion 

AdaBoost is a powerful boosting algorithm that combines weak learners to create a strong model, focusing on difficult-to-classify instances. It enhances prediction accuracy, reduces bias and variance, and is effective for tasks like classification, spam detection, and image processing, making it a valuable tool in machine learning.