Boosted models are a class of ensemble learning methods used in machine learning to improve predictive accuracy by combining multiple weak learners (typically decision trees) into a strong model. These models are built sequentially, where each new model corrects the errors of the previous one, leading to reduced bias and variance.

Key Concepts of Boosting

1. Sequential Learning – Models are trained iteratively, with each subsequent model focusing on misclassified instances.
2. Weighting – Misclassified data points are given higher weights to ensure the next model pays more attention to them.
3. Aggregation – Predictions from all models are combined, usually through weighted voting or averaging.
4. Overfitting Control – Regularization techniques like learning rate control and early stopping help prevent overfitting.

Popular Boosted Models

1. AdaBoost (Adaptive Boosting)

   • Assigns weights to instances and adjusts them iteratively.
   • Uses decision stumps (one-level decision trees) as weak learners.
   • Example: Used in face detection applications.

2. Gradient Boosting Machines (GBM)

   • Uses gradient descent optimization to minimize loss.
   • More flexible than AdaBoost but computationally intensive.
   • Example: Used in financial risk modeling and ranking systems.

3. XGBoost (Extreme Gradient Boosting)

   • An optimized version of GBM with speed and efficiency improvements.
   • Handles missing values and prevents overfitting using regularization.
   • Example: Frequently used in Kaggle competitions and structured data problems.

4. LightGBM (Light Gradient Boosting Machine)

   • Uses a histogram-based algorithm for faster training.
   • Grows trees leaf-wise instead of level-wise, improving efficiency.
   • Example: Ideal for large datasets with high-dimensional features.

5. CatBoost (Categorical Boosting)

   • Specially designed for handling categorical data efficiently.
   • Avoids one-hot encoding, making it memory-efficient.
   • Example: Used in e-commerce recommendation systems.

Advantages of Boosted Models

1. High predictive accuracy.
   
2. Works well with structured/tabular data.
   
3. Handles both regression and classification tasks.
   
4. Less prone to overfitting compared to deep models with proper tuning.

Disadvantages of Boosted Models

1. Computationally expensive.
   
2. Sensitive to noisy data and outliers.
   
3. Requires careful hyperparameter tuning.