AdaBoost Implementation
├── Introduction
│   └── Overview of AdaBoost
├── Setting Up the Environment
│   ├── Importing Libraries
│   └── Loading the Dataset
├── Implementing AdaBoost
│   ├── Data Preparation
│   ├── Model Training
│   └── Model Evaluation
├── Visualization
│   └── AdaBoost Classification Visualization
└── Conclusion
    └── Insights and Observations

1. Introduction

Overview of AdaBoost

• AdaBoost, short for Adaptive Boosting, is an ensemble learning method that combines multiple weak classifiers to create a strong classifier. It adapts by adjusting the weights of misclassified data points.

2. Setting Up the Environment

Importing Libraries

# Python code to import necessary libraries
import numpy as np
import matplotlib.pyplot as plt
from sklearn.ensemble import AdaBoostClassifier
from sklearn.model_selection import train_test_split
from sklearn.datasets import make_classification

Loading the Dataset

# Python code to load a sample dataset
X, y = make_classification(n_samples=100, n_features=2, n_redundant=0, n_clusters_per_class=1, random_state=6)

3. Implementing AdaBoost

Data Preparation

# Python code to split the data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

Model Training

# Python code to train the AdaBoost model
model = AdaBoostClassifier(n_estimators=100)
model.fit(X_train, y_train)

Model Evaluation

# Python code to evaluate the model
y_pred = model.predict(X_test)

4. Visualization

AdaBoost Classification Visualization