Feature vs Model parameter

Why they differ

Features are the values an example brings to the model; model parameters are the values the model learns and keeps from training.

Feature

Machine learning basics

One measurable fact about an example, such as a price or an age, that a model reads as input when it makes a guess.

Formal

A single input value describing an example, given to a model as a number or turned into numbers first; in training data each example is a row of features, often paired with a label.

In plain English

Like the boxes on a form, such as age, income and address, that a bank clerk reads before deciding on a loan.

In practice

A housing company that wants to guess the rent a flat can fetch gives its model the size in square metres, the number of rooms, the floor and the distance to the nearest train station.

Why it matters

A model can only find patterns in what it is shown, so a missing, wrong or unfair input limits every answer it gives.

Model parameter

Training & optimisation

One of the numbers inside a model that is set by learning from data; their count is how model size is usually stated.

Formal

An internal value of a model, mostly the model weights plus small offset values, that model training adjusts to reduce the loss; a large language model can have billions of them.

In plain English

Like the tiles in a huge mosaic; no single tile means much, but together their colours make the picture, and more tiles allow finer detail.

In practice

An IT buyer in a municipality compares a “7B” and a “70B” model for an internal assistant; the figures are billions of model parameters and give a rough guide to memory needs, running cost and ability.

Why it matters

Parameter count drives how much hardware, energy and money a model needs to train and run, and a larger count is no guarantee of a better result on a given task.