Crack ML Interviews with Confidence: CatBoost (25 Q&A)

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How to train a ML model using CatBoost in 5 steps:

• Initialize the model with multiple decision trees: CatBoost starts with a simple prediction and prepares to build an ensemble of decision trees sequentially.
• Calculate prediction errors (residuals): The algorithm compares current predictions with actual values and identifies the errors that need to be corrected.
• Build a new decision tree to reduce errors: A new tree is trained specifically to learn the residual errors from the previous prediction stage.
• Update predictions using gradient boosting: The predictions from the new tree are added to the existing model with a learning rate, gradually improving overall accuracy step by step.
• Handle categorical data efficiently and repeat iterations: CatBoost uses ordered encoding techniques to process categorical variables while repeatedly adding trees until the model reaches optimal performance or stopping criteria.

Let’s check your basic knowledge of CatBoost. Here are 20 Q&A for your next interview.