What is cross-validation and why is it used in ML?

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Khushi Singh · Answer

Cross-validation shows how well 
machine learning models can predict new data by dividing the dataset into multiple parts and testing them. The method tests if our model works correctly on new data sets besides just the data it learned from. Using this approach helps create models that avoid both excessive and insufficient adjustments.
The dataset divides into different smaller parts or folds for cross-validation. K-fold cross-validation employs an equal split of data into specific numbers of parts. During training the model needs k-1 subsets while testing is performed on one subset. The test set receives one fold each during k consecutive runs through the data splitting process. We calculate the final evaluation result through averaging of all K measurements to arrive at a dependable performance estimate for our model.
People use cross-validation for these specific purposes:
Performance Estimation uses data splits for accurate predictions on new information.
and The model selection feature supports picking the most effective algorithm between all tested options.
and It enables both grid search and randomized search approaches to find the best model parameters.
Cross-validation helps developers lower their models' vulnerability to overfitting and creates effective designs for practical applications.

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What is cross-validation and why is it used in ML?

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