What does it mean to train a machine learning model?

Training a machine learning model means using data to adjust the model so that it learns patterns that help it produce better predictions or decisions.

During training, the model processes examples, makes predictions, compares those predictions with the desired outcome when one is available, and updates its internal parameters to reduce its errors.

Simple example

Suppose we want to train a model to predict house prices.

The training data might contain examples such as:

House features → Actual price

For each example, the model:

• receives the input features
• produces a predicted price
• compares the prediction with the actual price
• measures how wrong the prediction was
• adjusts its learned parameters to improve future predictions

This process is repeated across many examples.

What changes during training?

The structure of the model may stay the same, but its learned parameters change.

For example:

• a linear model updates its coefficients
• a decision tree learns useful split points
• other models adjust their own internal parameters according to their learning algorithm

Training does not mean memorizing

The goal is not simply to remember the training examples. A useful model should learn patterns that also work on new data it has not seen before.

That ability is called generalization and is covered separately.

In short, training is the process of using data to adjust a model's learned parameters so that it becomes better at performing its intended task.