Hello and welcome! In today's lesson, we will explore the practice of Using Early Stopping to Prevent Overfitting. This technique is essential in ensuring that your gradient boosting models stay robust and accurate. We will introduce early stopping, revise data preparation steps, implement early stopping in a gradient boosting model, evaluate its performance, and visualize the predictions vs. actual values.

By the end of this lesson, you will understand how to effectively use early stopping to manage overfitting in your models, especially within the context of financial data.

Early stopping is a regularization technique used to prevent overfitting in machine learning models, particularly those that learn iteratively, like gradient boosting models. It works by monitoring the model's performance on a validation set during training and halting the training process when no significant improvement is observed over a specified number of iterations.

Overfitting occurs when a model learns the noise in the training data rather than the actual signal, resulting in poor generalization to new, unseen data. Early stopping can help mitigate this by terminating the training process before the model becomes too specialized in the training data.

Why Early Stopping?

• It enhances model generalization.
• Reduces training time by halting unproductive iterations.
• Helps manage resources efficiently.

Given that you already know how to load, prepare, and scale features, let's do a quick revision. We'll use the load_dataset function to load the TSLA dataset, create new features, and standardize them.