Here’s your chance to prove what you learned
Ready? Let’s test your Day-26 knowledge
Question 1 of 4
Leaf-wise growth creates more balanced trees
Leaf-wise growth adds nodes to all branches simultaneously
Leaf-wise growth focuses on the most promising branches first, creating deeper, optimized trees
Leaf-wise growth creates wider trees with more branches at each level
Question 2 of 4
It creates more accurate predictions by using advanced statistical methods
It bins continuous features to reduce memory usage and speed up computations
It eliminates the need for gradient boosting entirely
It automatically selects the best features without user input
Question 3 of 4
num_leaves should equal max_depth
num_leaves should be less than or equal to 2^max_depth - 1
num_leaves should always be at least 100
num_leaves has no relationship to max_depth
Question 4 of 4
When would you choose LightGBM over XGBoost for a machine learning project?
When you need a more robust model that works well on any dataset size
When your priority is having the most established algorithm with the longest history
When you're working with massive datasets and processing speed is critical
When you need maximum built-in protection against overfitting