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Data Science and Machine Learning Interview Handbook

Ensemble Methods

Explore how ensemble methods enhance machine learning models by combining multiple weak learners. This lesson covers bagging, including random forests with decision stumps, and boosting techniques like AdaBoost, focusing on how they reduce errors and improve predictions. You will understand implementation details, strengths, limitations, and practical comparisons to apply these methods effectively.

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When a single model isn’t quite enough, ensemble methods step in. Bagging reduces variance by combining models trained on random subsets of data, while boosting focuses on learning from mistakes to improve accuracy. In this lesson, we’ll get hands-on with both strategies and understand how they power some of the best models in machine learning. Let’s begin.

Implement a random forest classifier

You’re asked to implement an ensemble classifier from scratch using bagging principles. Your task is to build a simplified Random Forest that aggregates predictions from multiple decision trees trained on bootstrapped samples of the training set. Use an object-oriented approach to implement the classifier, train it on a dataset, and make predictions.

This question is frequently asked in Systems design and ML interview rounds to evaluate object-oriented design and ensemble strategy understanding.

Sample answer

Ensemble methods combine multiple machine learning models to improve prediction performance beyond what could be achieved by any single model. These techniques are powerful tools that can significantly enhance the accuracy, stability, and robustness of predictive models.

Ensemble methods work by training multiple models and combining their predictions. The key insight behind ensemble learning is that different models capture different aspects of the data, and by combining them, we can create a more comprehensive and accurate predictive system. Ensemble methods generally fall into two categories: bagging (parallel ensemble) and boosting (sequential ensemble).

Bagging (bootstrap aggregating) creates multiple versions of a model ...