Now that we understand why we need to evaluate machine learning models and the basic types of problems they solve (classification and regression), let's look at the typical steps involved in the evaluation process itself. Think of this as a roadmap for checking how well your model performs.

At a high level, evaluating a machine learning model generally follows these steps:

Data Preparation: You start with your dataset. The important first step here is splitting this data into at least two parts: a training set and a test set.
Model Training: You use the training set to teach your model. The model learns patterns, relationships, or rules from this data.
Model Prediction: You take the trained model and use it to make predictions on the test set. Because the model has never seen this test data before, these predictions give us a fair assessment of its capabilities.
Performance Evaluation: You compare the model's predictions on the test set to the actual, true values in the test set. This is where evaluation metrics come in. You calculate specific scores (like accuracy for classification or mean squared error for regression) that quantify the model's performance.
Result Interpretation: Finally, you analyze the metric scores. Do they meet your requirements? Is the model good enough for its intended purpose? This step helps you decide if the model is ready or if it needs further improvement.

Let's visualize this basic flow:

A simplified view of the machine learning model evaluation workflow.

The most significant principle here is evaluating the model on data it hasn't encountered during training (the test set). This separation helps prevent overly optimistic results and gives a more realistic estimate of how the model will perform in real-world scenarios when faced with new data.

In the upcoming chapters, we will examine the specific metrics used in step 4 for both classification (Chapter 2) and regression (Chapter 3) problems. We will also look more closely at data splitting techniques (Chapter 4) to ensure your evaluation is reliable. This chapter provides the foundational understanding of why and how, in general terms, we approach model evaluation.