How to plot Validation Curve in Python

When building a machine learning model, it’s important to evaluate its performance and optimize its parameters. One way to do this is by plotting a validation curve.

A validation curve is a graph that shows the relationship between a model’s performance and a specific hyperparameter value. It helps to understand how the model’s performance improves as the hyperparameter value is adjusted.

In Python, the library scikit-learn provides an easy way to plot a validation curve using the function validation_curve().

The first step is to import the library and load the dataset into a pandas dataframe. Then, split the data into training and testing sets, and create an instance of the model you want to evaluate.

After that, use the validation_curve() function, which takes the model, the dataset, the name of the hyperparameter you want to adjust, and an array of values for that hyperparameter as inputs. The function returns two arrays: the train scores and the test scores for each value of the hyperparameter.

You can use the matplotlib library to plot the scores, by plotting the training and testing scores separately for each value of the hyperparameter. This gives you an idea of how the model’s performance improves as the hyperparameter value is adjusted.

In summary, a validation curve is a powerful tool for evaluating the performance of a machine learning model and optimize its parameters. By using the validation_curve() function in scikit-learn, it’s easy to plot a validation curve in Python, making it a valuable tool for data scientists and machine learning practitioners.

In this Learn through Codes example, you will learn: How to plot Validation Curve in Python.