What Is Cross validation?

Cross-validation is a technique for evaluating the performance of a predictive model by testing it on a subset of data that was not used to train the model. It is a method for assessing how well the model generalizes to new data.

Cross-validation is a common practice in machine learning and statistical modeling. It is used to assess the predictive accuracy of a model and to detect overfitting, which occurs when a model performs well on the training data but does not generalize well to new data.

The process involves splitting the dataset into two subsets: a training set and a test set. The model is trained on the training set and then evaluated on the test set. The results are compared to determine how well the model performs on new data.

Cross-validation can be used to compare different models or to tune the parameters of a model. It can also be used to select the best features for a model or to determine the optimal number of features.

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