Binary Logistic Regression Calculator
Fit binary outcomes against one numeric predictor using an explicit standardized-slope L2 penalty and evaluate a query probability.
YOUR CALCULATION
Result
—
Calculation steps
Work through one step at a time or reveal the complete calculation. The steps use your current inputs.
How to calculate
Standardize X and minimize summed logistic log loss plus a slope-only L2 term with damped Newton iteration.
p=1/(1+exp(−(b0+b1z))); objective=Σ[softplus(b0+b1z)−y(b0+b1z)]+λb1²/2
Worked example
The symmetric default data have complementary outcomes at X and −X. At query X=0 the fitted probability is 0.5, including the entered λ=1 penalty.
Assumptions and input details
4–500 complete observations, one varying numeric X, and both outcome classes. X magnitude is at most 1e9. λ must be 0.001–1000; the intercept is unpenalized. Standardization uses population RMS about the observed mean and the penalty applies in that basis. No maximum-likelihood coefficient tests, calibration validation, causal interpretation or classification threshold is supplied. Results depend on λ and the sample.
References: Google ML: logistic log loss and regularization
Using this calculator
Can I enter my own problem?
Yes. Replace the example values with your own inputs. The result, calculation steps, tables and applicable diagrams update from those inputs. Blank or invalid values clear the result; example values are never silently substituted for missing inputs.
Are the results exact?
Results use floating-point arithmetic and displayed rounding. Read the formula, assumptions and any stated data precision before interpreting the result.
Is my input uploaded?
The calculation runs in your browser. No account, file upload or external AI service is required. Inputs are not saved between page reloads.
Fit an intercept and two to five numeric predictors, inspect original-unit coefficients and evaluate a predictor query.
Build a tied-score ROC sweep and calculate trapezoidal AUC plus noninterpolated average precision from labeled numeric scores.
Calculate precision, recall, specificity, F1, accuracy, balanced accuracy and MCC from a declared positive-class count table.
Reserved advertising space.