How to use cross_val_score for Cross Validation in Keras

Cross-validation is a technique used to evaluate the performance of a machine learning model by dividing the dataset into different subsets and training and evaluating the model on different subsets of the data. This can provide a more robust estimate of the model’s performance.

In Keras, you can use the cross_val_score function from the sklearn library to perform cross-validation. This function takes several arguments, such as the model, the dataset, the scoring metric, the number of folds, and the cross-validation technique to use. It returns an array of scores, one for each fold.

The first step to use cross_val_score in Keras is to create a function that creates and compiles the model. Next, you need to create an instance of the cross_val_score function and pass it the model, the dataset, the scoring metric, the number of folds, and the cross-validation technique to use.

The scoring metric used to evaluate the model’s performance, can be chosen from a list of predefined metrics such as accuracy, F1 score, precision, recall, etc. or it can be a custom scoring function.

Once you have created an instance of the cross_val_score function and passed it the necessary arguments, you can call the function with the training data as an argument. The cross_val_score function will then train and evaluate the model using the specified cross-validation technique, and return an array of scores, one for each fold.

It’s important to note that cross_val_score is a convenient function that wraps several steps and calculations that are required for cross-validation, it can save computational time and it’s easy to use.

In summary, to use cross_val_score for cross-validation in Keras, you need to create a function that creates and compiles the model, create an instance of the cross_val_score function and pass it the model, the dataset, the scoring metric, the number of folds, and the cross-validation technique to use, then call the function with the training data as an argument. The cross_val_score function will then train and evaluate the model using the specified cross-validation technique and return an array of scores, one for each fold.

In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in Python programming: How to use cross_val_score for Cross Validation in Keras.

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