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Binary Classification: Logistic Regression

Explore how logistic regression estimates class probabilities, draws a decision boundary, and turns probabilities into predictions using a threshold.

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Section A

Explore the dataset

Predict whether a student passes based on study hours and practice tests. Class 1 is pass; class 0 is fail. Incomplete rows are ignored.

Study hoursPractice testsOutcomeAction

14 complete observations

Section C

Decision boundary

The shaded regions show the predicted class; the line marks where the model probability equals the selected threshold.

Study 1 hours, 1 practice tests, actual class 0Study 1.5 hours, 2 practice tests, actual class 0Study 2 hours, 1 practice tests, actual class 0Study 2.5 hours, 3 practice tests, actual class 0Study 3 hours, 2 practice tests, actual class 0Study 3.5 hours, 4 practice tests, actual class 0Study 4 hours, 3 practice tests, actual class 0Study 4 hours, 5 practice tests, actual class 1Study 5 hours, 4 practice tests, actual class 1Study 5.5 hours, 6 practice tests, actual class 1Study 6 hours, 5 practice tests, actual class 1Study 6.5 hours, 7 practice tests, actual class 1Study 7 hours, 6 practice tests, actual class 1Study 8 hours, 7 practice tests, actual class 1Study hoursPractice tests
Fail (class 0) Pass (class 1) Decision boundary

Section D

Train and evaluate

Ready for training.

Log loss

0.693

Accuracy

50.0%

Precision

50.0%

Recall

100.0%

Confusion matrix · rows are actual, columns are predicted

Predicted failPredicted pass
Actual fail07
Actual pass07

Training step 0 of 100 · probabilities become labels at the selected threshold.