Simple Ensemble Methods

Types of Simple Ensemble Methods

a. Voting (Majority Voting): Voting is one of the simplest ensemble methods used for classification tasks. It involves combining the predictions of multiple individual models and selecting the class with the majority vote. In the case of binary classification, if one model predicts Class A, and another predicts Class B, voting would choose the class that receives more votes. It can be performed with equal weights for all models (hard voting) or with weighted votes based on the models' confidence scores (soft voting).
b. Averaging: Averaging is commonly used for regression tasks. Instead of voting for discrete classes, it combines the predictions of multiple models by taking the average of their predicted continuous values. This approach helps reduce the variance of predictions and can lead to more stable and accurate results.
c. Bagging (Bootstrap Aggregating): Bagging is a simple ensemble method that involves training multiple instances of the same base model on different random subsets of the training data (sampling with replacement). Each model is then trained independently, and the final prediction is made by averaging (for regression) or voting (for classification) the predictions of all individual models. Random Forest is a popular implementation of bagging, using decision trees as base learners.
d. Boosting: Boosting is another simple ensemble method, but unlike bagging, it focuses on sequentially training multiple models, where each subsequent model corrects the errors made by its predecessors. Boosting assigns weights to training samples and gives more importance to misclassified samples in the training process. As a result, it iteratively creates stronger models. Common boosting algorithms include AdaBoost and Gradient Boosting Machines (GBM).

Advantages & Dis-Advantages

1. Easy Implementation: Simple ensemble methods are relatively easy to implement and understand, making them accessible to both beginners and experienced practitioners.
2. Low Computational Complexity: Since they do not involve complex algorithms or dependencies, simple ensemble methods generally have lower computational complexity compared to advanced ensemble methods.
3. Majority Voting and Averaging: Simple ensemble methods often rely on majority voting for classification tasks, where the final prediction is determined by the majority class predicted by individual models. For regression tasks, simple ensemble methods use averaging to obtain the final prediction.
4. Combining Weak Learners: Simple ensemble methods can effectively combine weak learners (models with slightly better performance than random guessing) to create a strong predictor, demonstrating the power of collective intelligence.
5. Prone to Overfitting: While simple ensemble methods can reduce overfitting compared to individual models, they are still susceptible to overfitting, especially when the base models are too complex or the dataset is small.