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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