What is the difference between bagging and boosting?
Theme: Machine Learning Concepts Role: Machine Learning Engineer Function: Technology
Interview Question for Machine Learning Engineer: See sample answers, motivations & red flags for this common interview question. About Machine Learning Engineer: Builds machine learning models and algorithms. This role falls within the Technology function of a firm. See other interview questions & further information for this role here
Sample Answer
Example response for question delving into Machine Learning Concepts with the key points that need to be covered in an effective response. Customize this to your own experience with concrete examples and evidence
- Definition: Bagging and boosting are both ensemble learning techniques used in machine learning
- Purpose: Bagging aims to reduce variance and prevent overfitting, while boosting focuses on reducing bias and improving accuracy
- Base Learners: Bagging uses parallel base learners, where each learner is trained independently on different subsets of the training data. Boosting uses sequential base learners, where each learner is trained to correct the mistakes made by the previous learners
- Weighting: In bagging, all base learners are given equal weightage during the final prediction. In boosting, base learners are assigned weights based on their performance, and their predictions are combined using weighted voting
- Training Process: Bagging trains base learners independently and in parallel. Boosting trains base learners sequentially, with each learner focusing on the instances that were misclassified by the previous learners
- Error Handling: Bagging handles errors by averaging the predictions of all base learners, which helps to reduce variance. Boosting handles errors by assigning higher weights to misclassified instances, forcing subsequent learners to focus on those instances and improve accuracy
- Final Prediction: In bagging, the final prediction is made by averaging the predictions of all base learners. In boosting, the final prediction is made by combining the weighted predictions of all base learners
- Algorithm Examples: Examples of bagging algorithms include Random Forest and Extra Trees. Examples of boosting algorithms include AdaBoost, Gradient Boosting, and XGBoost
Underlying Motivations
What the Interviewer is trying to find out about you and your experiences through this question
- Knowledge of machine learning algorithms: Understanding the differences between bagging and boosting algorithms
- Problem-solving skills: Ability to identify appropriate algorithmic techniques for different scenarios
- Critical thinking: Analyzing the strengths and weaknesses of different ensemble learning methods
- Understanding of model performance: Awareness of how bagging and boosting impact model accuracy and generalization
Potential Minefields
How to avoid some common minefields when answering this question in order to not raise any red flags
- Lack of understanding: Providing incorrect or vague definitions of bagging and boosting
- Confusion: Mixing up the concepts or using them interchangeably
- Limited knowledge: Inability to explain the advantages and disadvantages of bagging and boosting
- Lack of practical experience: Unable to provide real-world examples or use cases for bagging and boosting
- Overconfidence: Claiming to know everything about bagging and boosting without acknowledging any limitations or challenges