This discussion will cover the difference between validation and evaluation in scikit-learn. In the fields of Machine learning and Data Science, Validation and Evaluation are two key ideas. Despite the frequent interchangeability of the two names, they serve distinct functions and have separate meanings.
1. Validation
Validation is the process of assessing the performance of a model during the training process.It involves splitting the available data into two parts: a training set and a validation set. The model is trained on the training set, and its performance is evaluated on the validation set. The purpose of validation is to assess the performance of the model on data that it has not seen before, and to determine if the model is overfitting or underfitting the training data.
In this example, we load the iris dataset using the load_iris() function, and split it into training and validation sets using the train_test_split() function. We then create a KNeighborsClassifier model, fit it to the training data, and use it to predict on the validation data. Finally, we calculate the accuracy score on the validation data using the accuracy_score() function.
2. Evaluation
Evaluation is the process of assessing the performance of a trained model on a separate test set. The purpose of evaluation is to determine how well the model is likely to perform on new, unseen data. This is important because a model that performs well on the training and validation data may not necessarily perform well on new data.
In this example, we load the iris dataset using the load_iris() function, create a KNeighborsClassifier model, and fit it to the entire dataset. We then use the model to predict on new, unseen data, and calculate the accuracy score on the test data using the accuracy_score() function. Note that in this case, we don’t split the data into training and validation sets, since we’re evaluating the performance of the model on new, unseen data.