Applied Data Science Coding in Python: Cross Validation

Applied Data Science Coding in Python: Cross Validation

Cross validation is a technique used in machine learning to evaluate the performance of a model. It is used to measure how well a model will perform on unseen data. The idea behind cross validation is to divide the data into several subsets, train the model on some of the subsets, and then evaluate the model on the remaining subsets.

There are several different types of cross validation, but the most commonly used one in Python is called “k-fold cross validation.” This method divides the data into k subsets, also known as “folds.” The model is trained on k-1 of the folds, and then evaluated on the remaining fold. This process is repeated k times, with a different fold being used as the evaluation set each time. The final performance score is the average of the performance on each fold.

Another common technique is called “Leave One Out Cross Validation” (LOOCV) which is similar to k-fold cross validation, but instead of dividing the data into k subsets, a single observation is used as the validation set while the rest of the data is used for training.

Cross validation is a powerful technique that can help you get a more accurate picture of how well a model will perform on unseen data. It is especially useful when you have a limited amount of data, as it allows you to use all of the data for training and testing.

It’s important to note that cross validation is a technique that should be applied after you have preprocessed your data, selected your features and chosen an appropriate model.

In summary, cross validation is a technique used in machine learning to evaluate the performance of a model on unseen data. It is usually done by dividing the data into subsets, training the model on some subsets and then evaluating the model on the remaining subsets. Two popular methods of cross validation are k-fold cross validation, and Leave One Out Cross Validation (LOOCV). These methods are implemented in python using libraries such as scikit-learn and caret.

 

In this Applied Machine Learning & Data Science Recipe, the reader will learn: How to do Cross Validation.



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