Load a CSV and inspect it
Start with a CSV that has numeric predictors and one binary target column (0/1). One row means one observation.
Tool
Logistic Regression Lab
This guide teaches logistic regression in five focused steps. Upload one CSV, choose a binary target, train live in your browser, and interpret class probabilities clearly.
Guided Logistic Regression Tool
Guided Learning
Step 1 of 5
- 01 Load
- 02 Select
- 03 Base
- 04 Train
- 05 Explain
Upload a CSV file with at least two numeric columns, including one binary target.
Question 1
Question 2
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Pick the binary output column (values 0/1).
X (Predictors)
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Pick one binary target (`0/1`) and at least one predictor.
Question 3
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This is your before-training snapshot.
Samples
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Number of rows with valid numeric values for all selected predictors and the target.
Features
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Number of selected predictors and matrix shape used to train the model.
Class Balance
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Number of negative and positive rows in the selected binary target.
Mean |r|
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Average absolute Pearson correlation between each predictor and target.
Log Loss
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Binary cross-entropy loss. Lower means predicted probabilities are better calibrated.
Accuracy
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Fraction of correctly classified rows using a 0.5 probability threshold.
Question 4
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Iteration0/0
Gradient Norm--
Current Log Loss--
Current Accuracy--
Best Log Loss--
Best Accuracy--
Training Log
Awaiting dataset...
Question 5
Prediction
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Enter all predictor values to estimate class probability.
Interpretation
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Equation will appear after training.
Complete training to unlock prediction and plain-language interpretation.
Coefficient Meaning
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Bias Meaning
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Model Quality
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Plain-English Summary
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Free logistic regression calculator for binary classification
Logistic Regression Lab teaches binary classification from CSV data. Upload a dataset, choose numeric predictors and a 0/1 target, then watch the model learn probabilities while the page explains accuracy, log loss, coefficients, and prediction confidence.
What you can learn
Probability prediction
Learn why logistic regression predicts probabilities before turning them into class labels.
Binary targets
Use 0/1 target columns to model decisions such as no/yes, fail/pass, or inactive/active.
Classification metrics
Track log loss and accuracy while training, then interpret the final classifier clearly.
FAQ
What is logistic regression?
It is a classification method that estimates the probability of an example belonging to a positive class.
What target values should I use?
Use a binary target column with values like 0 and 1 so the model can learn two classes.
What is log loss?
Log loss measures how good the predicted probabilities are. Lower log loss usually means better calibration.
Is this only for experts?
No. The guided interface is designed for beginners who want to understand classification step by step.