Logistic Regression

Logistic regression is the workhorse of classification. Watch it bend a straight line into an S-curve that outputs a clean probability.

Logistic RegressionLive

probability from a straight line

Controls

Presets

Weight w (steepness)

Bias b (shift)

Decision threshold

Logistic regression squashes a linear score through the sigmoid to output a probability between 0 and 1. Predictions above the threshold become one class, below it the other. Raising the weight sharpens the S-curve into a harder decision. Educational tool.

▶ Run in Python

Data Inspector

Decision boundary at x0.50

P(class 1 | x=1)0.82

Threshold0.50

Governing equation

p (x) = \frac{1}{1 + e ^{- (- 1.50 + 3.0 x)}} = 0.5 \Rightarrow x^{*} = 0.50

Reading this result: Logistic regression fits this sigmoid by gradient descent on cross-entropy. Weight w=3 sets steepness, bias b=-1.5 shifts the curve, placing the decision boundary at x=0.50 for threshold 0.5.

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How it works

Logistic regression computes a weighted score and passes it through the sigmoid to produce a probability between 0 and 1. A threshold turns that probability into a class. Despite the name it is a classifier, and it underpins everything from credit scoring to medical risk models. Educational tool.

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Frequently asked questions

Is this logistic regression simulator tool really free?▾

Yes. Logistic Regression runs entirely in your browser using your device's own compute, so local use is free forever. You only pay Compute Tokens if you scale a job to the cloud.

Do I need to install anything?▾

No. Everything runs client-side in a modern browser — no downloads, no license, no account required to start.

Can I save or share my simulation?▾

Create a free account to save projects, and use a shareable embed or minted DOI to publish a live, interactive version anywhere.

How accurate are the results?▾

The solver uses established numerical methods, but results are for research and educational purposes and should be validated against experiment or professional review before you rely on them.