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These topics will be covered in this module’s knowledge check and entry ticket.

• Understand traditional machine learning models and the differences between linear regression, decision trees and random forests.
• Understand the importance of model validation and testing the model’s performance on unseen data.
• Explain the concept of mean squared error (MSE) as a metric to evaluate the performance of a model.
• Interpret differences in MSE scores and their implications for model performance.
• Explain the concept of overfitting and why it can be a problem for ML models.
• Implement the train-test split technique using scikit-learn’s train_test_split function to divide the dataset into separate training and testing subsets.
• Train a linear regression model on the training data, and evaluate its performance on the unseen testing data using MSE. (You will not have to write functional code during entry tickets, but make sure you understand the process such that you could explain it or interpret what some Python / Pandas code does).