What is Cross-Validation
Cross-validation is a fundamental technique used in machine learning to assess a model’s performance by mitigating the risk of overfitting and determining how well a model is likely to generalize to unseen data. This process involves several steps dividing the dataset into multiple subsets or folds, then training the model on the training set, and finally evaluating its performance on the remaining validation set. Two common cross-validation methods are k-fold cross-validation and stratified k-fold cross-validation, The stratified CV is going to be used in this article. There are some key-benefits of cross-validation listed below–>Robust Performance Assessment: Cross-validation provides a more accurate estimate of a model’s performance because it assesses its ability to generalize to different data subsets which helps to detect issues like overfitting.
- Hyperparameter Tuning: Cross-validation is important for hyperparameter tuning. By evaluating model performance across various parameter combinations, data scientists can identify the best hyperparameters which results optimal model performance and accurate prediction.
- Effective Use of Data: Cross-validation ensures that the entire dataset is used for both training and validation which maximizes the utility of the available data.
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.