What is a machine learning model?

A machine learning model is a learned mathematical or computational representation that takes input data and produces an output such as a prediction, score, category, or decision.

Instead of containing only hand-written rules, the model contains patterns learned from training data.

Simple way to think about it

A model acts like a function:

Input features → Machine learning model → Output

For example, a house-price model might receive:

• size of the house
• number of bedrooms
• location
• age of the property

and produce:

Predicted price → ₹75,00,000

What does a model contain?

What a model stores depends on the algorithm being used.

For example:

• A linear model may store learned coefficients.
• A decision tree stores learned decision rules and split points.
• More complex models may contain many learned parameters that capture relationships in the data.

These learned values determine how the model converts new inputs into outputs.

Model vs algorithm

A learning algorithm is the procedure used to learn from data. The model is the result produced after that learning process.

A simple view is:

Training data + Learning algorithm → Trained model

Once trained, the model can be given new data and used to make predictions.

In short, a machine learning model is the learned representation that uses patterns from data to convert new inputs into useful outputs.