Why to perform Hyperparameter tuning
Hyperparameter tuning is the process of systematically searching for the best hyperparameter values for a machine learning model which has several key-importance listed below:
- Improved Model Performance: The right set of hyperparameters can significantly enhance a model’s performance which leads to better accuracy and generalization to new data.
- Reduction of Overfitting: Carefully chosen hyperparameters can prevent overfitting where the model learns to fit the training data too closely and performs poorly on unseen data which leads to wrong predictions on large datasets.
- Increased Robustness: Tuned hyperparameters make a model more resilient to variations in the data and different problem scenarios which ensures it to remain effective in various situations by reducing computational resources and training time.
- Enhanced Interpretability: Some hyperparameters can influence the interpretability of the model and tuning them can make the model’s output more understandable and actionable which leads to accurate predictions with optimized model training.
CatBoost Cross-Validation and Hyperparameter Tuning
CatBoost is a powerful gradient-boosting algorithm of machine learning that is very popular for its effective capability to handle categorial features of both classification and regression tasks. To maximize the potential of CatBoost, it’s essential to fine-tune its hyperparameters which can be done by Cross-validation. Cross-validation is a crucial technique that allows data scientists and machine learning practitioners to rigorously assess the model’s performance under different parameter configuration sets and select the most optimal hyperparameters. In this article, we are going to discuss how we can tune the hyper-parameters of CatBoost using cross-validation.