I am currently working on a machine learning project using Scikit-Learn, and I am having trouble with model evaluation and validation. I have split my data into training and testing sets using the train_test_split() function, and I have trained my model using a pipeline that includes preprocessing steps such as feature scaling and feature selection.
Here is the code I have so far:
However, I’m not sure if I’m doing the evaluation and validation correctly. Can someone help me understand the difference between model evaluation and validation in scikit-learn, and suggest any improvements to my code? Thank you!
In machine learning, understanding validation and evaluation is essential. Below, I will explain both concepts separately to provide clarity and aid comprehension.
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 the validation data. Finally, we calculate the accuracy score on the validation data using the accuracy_score() function.
Evaluation
Evaluation is the process of assessing the performance of a trained model on a **separate test set**. The purpose of the 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 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.