What are some techniques used to evaluate and validate ML models?

Evaluating and validating machine learning (ML) models is a crucial step in the model development process to ensure their reliability, performance, and generalization capabilities. Several techniques are commonly employed to assess and validate ML models. Here are some of the key techniques: 1. Train-Test Split: The train-test split is a basic technique where the dataset is divided into two subsets: the training set and the testing set. The training set is used to train the ML model, while the testing set is used to evaluate its performance. The split is typically done in a stratified manner to ensure a representative distribution of classes or data characteristics in both sets. 2. Cross-Validation: Cross-validation is a more robust technique to estimate the performance of ML models. It involves dividing the dataset into multiple subsets (folds). The model is trained and evaluated multiple times, each time using a different fold as the testing set and the remaining folds as the training set. This technique helps in obtaining a more reliable estimate of the model's performance and reduces the impact of dataset variability. 3. Evaluation Metrics: Various evaluation metrics are used to quantify the performance of ML models, depending on th....