Linear regression vs Logistic regression

Why they differ

Linear regression predicts a number such as a price; logistic regression, despite its name, predicts which group something belongs to.

Linear regression

Machine learning basics

A simple model that predicts a number by adding up each input times its own weight, choosing the weights that fit past examples best.

Formal

A form of regression that predicts a number as a weighted sum of the features plus a fixed starting value, with the weights chosen to make the squared errors on the training data as small as possible.

In plain English

Like guessing the price of a flat from its size by drawing the straight line that passes as close as possible to all the flats you already know the price of.

In practice

A Danish energy company predicts next month's power use for each home from floor area, number of people and last year's use, and can read off how much each extra person adds.

Why it matters

It is fast, easy to check and easy to explain, so it is the baseline every more complex model must beat, and a common choice when a decision has to be justified.

Logistic regression

Machine learning basics

A simple model that sorts cases into two groups by turning a weighted sum of the inputs into a chance between 0 and 1.

Formal

A method for classification that adds up the features, each times a weight, and passes the total through an S-shaped curve to get the chance of the positive group; the weights are chosen so the known answers in the training data get the highest chance.

In plain English

Like a doctor adding up points for age, smoking and blood pressure, then reading off a chart that turns the total score into a chance of illness that never goes below zero or above certain.

In practice

A Danish bank scores each loan request with the chance that the customer will fail to pay, and staff can see which answers on the form pushed the score up or down.

Why it matters

It gives a chance, not just a yes or no, and each input's effect can be read directly, which is why banks, hospitals and public bodies still rely on it when decisions must be explained.

Shared connections

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