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4 cards from this section. Quick, good the night before a test.
What is boosting in machine learning?
Think of how models can learn from their mistakes.
Name one popular boosting algorithm.
Which boosting algorithm can you recollect from our discussions?
What is the primary focus of boosting?
What do we learn from our mistakes?
True or False: Boosting can help reduce both bias and variance.
What advantages does boosting provide?
Develop a small dataset and train an AdaBoost model. Analyze the results based on misclassifications and weights assigned during the model training.
Remember to track weights and focus on how they change after each iteration.
Compare the performance of Gradient Boosting versus XGBoost on a given dataset and discuss the differences you observe in training time and accuracy.
Focus on speed and efficiency vs. performance in your evaluation.
6 more questions in this section. Enroll free to practise them all.
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