# Logistic Regression Calculator Fit a binary logistic regression model with gradient descent and predict class probabilities > Canonical page: https://elysiatools.com/en/tools/logistic-regression-calculator - **Category:** Math & Numbers - **Keywords:** logistic regression, binary classification, probability, gradient descent, classifier, calculator ## Overview The Logistic Regression Calculator is a specialized tool for fitting binary classification models using gradient descent. It allows users to input CSV data containing numeric predictors and a binary target to calculate model accuracy and predict outcomes for new data points based on a customizable probability threshold. ## Inputs - **CSV Data** (textarea): Predictors first, binary target last, e.g. x1,x2,y 0,1,0 1,2,0 3,2,1 - **Has Header Row** (checkbox) - **Prediction Values (Optional)** (text): e.g. 4, 3 - **Learning Rate** (number) - **Iterations** (number) - **Threshold** (number) - **Decimal Places** (number) ## When to use - When you need to model the relationship between multiple independent variables and a single binary outcome. - When you want to predict the probability of an event occurring based on historical numeric data. - When you require a lightweight way to perform binary classification without complex statistical software. ## How it works - Input your dataset in CSV format, ensuring the binary target variable (0 or 1) is located in the final column. - Configure the gradient descent parameters, including the learning rate and the total number of iterations for model training. - Set a classification threshold to determine the cutoff point for assigning predicted classes from calculated probabilities. - Optionally provide new predictor values to receive a specific class prediction and probability score for those inputs. ## Use cases - Predicting customer churn (yes/no) based on usage metrics and subscription length. - Estimating the likelihood of a loan default based on credit score and income data. - Classifying medical test results as positive or negative based on clinical biomarkers. ## Frequently asked questions ### What format should my data be in? Data should be provided as CSV text with numeric predictors in the first columns and the binary target (0 or 1) in the last column. ### What does the learning rate do? The learning rate controls the step size during gradient descent; a value too high may overshoot the optimum, while a value too low may converge slowly. ### Can I use categorical data? No, this calculator requires numeric predictors. Categorical data should be converted to numeric values before input. ### What is the threshold setting? The threshold is the probability value (default 0.5) used to decide if a result belongs to class 1 or class 0. ### How is model accuracy calculated? Accuracy is determined by comparing the model's predictions against the actual target values provided in the training dataset. ## Related tools - [Multiple Linear Regression Calculator](https://elysiatools.com/en/tools/multiple-linear-regression-calculator): Fit a multiple linear regression model from rows of predictor variables and a numeric target - [Column Calculator](https://elysiatools.com/en/tools/column-calculator): Perform calculations on columns and add new rows - [Data Append Merger](https://elysiatools.com/en/tools/data-append-merger): Vertically append and merge multiple data tables with intelligent column matching - [Row Calculator](https://elysiatools.com/en/tools/row-calculator): Perform calculations on each row and add new columns - [Feature Scaler](https://elysiatools.com/en/tools/feature-scaler): Scale and normalize features using various methods for machine learning preprocessing and data standardization - [Markdown Table to CSV JSON with Type Inferred Schema](https://elysiatools.com/en/tools/markdown-table-to-csv-json-schema): Convert a Markdown pipe table to CSV, JSON rows or an inferred JSON Schema with alignment and null diagnostics - [CSV Protobuf Converter](https://elysiatools.com/en/tools/csv-to-protobuf): Convert between CSV and Protocol Buffers (Protobuf) formats - [Column Reorder](https://elysiatools.com/en/tools/column-reorder): Reorder table columns without changing the data rows ## Samples - [CSV Samples](https://elysiatools.com/en/samples/csv-samples): Sample CSV files with various data types, sizes, and complexity levels - [Python Samples](https://elysiatools.com/en/samples/python): Essential Python code examples and Hello World demonstrations - [Windows String Processing - C# Samples](https://elysiatools.com/en/samples/windows-string-processing-csharp): Comprehensive C# string processing examples for Windows platform including string manipulation, splitting, joining, regex operations, and text analysis - [Cypress E2E Testing Framework](https://elysiatools.com/en/samples/cypress-testing): Comprehensive Cypress end-to-end testing examples including test setup, page object models, API testing, visual regression testing, and advanced E2E patterns for modern web applications ## Related content - [CSV Export and Table Conversion Tools](https://elysiatools.com/en/hubs/csv-convert): Compare CSV to Excel, JSON, HTML, Markdown, XML, and text conversion tools in one hub for tabular export and interchange workflows. - [CSV Cleanup and Table Reshaping Tools](https://elysiatools.com/en/hubs/csv-utility): Compare CSV cleanup, filtering, sorting, grouping, merging, splitting, and table reshaping tools in one hub for spreadsheet and import/export workflows. - [Data Quality, Dedupe, and Anomaly Detection Tools](https://elysiatools.com/en/hubs/data-quality-anomaly-workflows): Profile CSV/JSON datasets, compare spreadsheet versions, find duplicates, outliers, missing-value issues, referential breaks, and time-series anomalies in one data-quality workflow hub.