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Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is boosting in machine learning?
💡 Hint: Think of how models can learn from their mistakes.
Question 2
Easy
Name one popular boosting algorithm.
💡 Hint: Which boosting algorithm can you recollect from our discussions?
Practice 4 more questions and get performance evaluation
Engage in quick quizzes to reinforce what you've learned and check your comprehension.
Question 1
What is the primary focus of boosting?
💡 Hint: What do we learn from our mistakes?
Question 2
True or False: Boosting can help reduce both bias and variance.
💡 Hint: What advantages does boosting provide?
Solve 2 more questions and get performance evaluation
Push your limits with challenges.
Question 1
Develop a small dataset and train an AdaBoost model. Analyze the results based on misclassifications and weights assigned during the model training.
💡 Hint: Remember to track weights and focus on how they change after each iteration.
Question 2
Compare the performance of Gradient Boosting versus XGBoost on a given dataset and discuss the differences you observe in training time and accuracy.
💡 Hint: Focus on speed and efficiency vs. performance in your evaluation.
Challenge and get performance evaluation