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Crossvalidation helps assess the model's generalization ability from "summary" of Machine Learning For Dummies by John Paul Mueller,Luca Massaron
Crossvalidation is a technique used to assess how well a machine learning model can generalize to new, unseen data. When you build a machine learning model, you want it to not only perform well on the training data but also on new data that it hasn't seen before. Crossvalidation helps you estimate how well your model will generalize by simulating the process of training and testing on different subsets of the data. The basic idea behind crossvalidation is to split your data into multiple subsets, or folds. You then train your model on a subset of the data and test it on a different subset. By repeating this process multiple times with different subsets, you can get a more robust estimate of how well your model will perform on new data. One common type of crossvalidation is k-fold crossvalidation, where the data is divided into k subsets of roughly equal size. The model is trained on k-1 of the subsets and tested on the remaining subset. This process is repeated k times, with each subset used as the test set exactly once. The performance of the model is then averaged across all k iterations to get an overall estimate of its generalization ability. Crossvalidation helps prevent overfitting, which occurs when a model performs well on the training data but poorly on new data. By testing the model on multiple subsets of the data, you can detect if it is overfitting to any particular subset and get a more accurate estimate of its generalization ability.- Crossvalidation is a powerful tool for assessing the generalization ability of a machine learning model. By simulating the process of training and testing on different subsets of the data, it provides a more reliable estimate of how well the model will perform on new, unseen data.
