Logistic Regression Calculator

Fit a binary logistic regression model with gradient descent and predict class probabilities

Example Results

1 examples

Fit a binary classifier

Train logistic regression from numeric predictors and a 0/1 target

{
  "result": {
    "accuracy": 1,
    "predictedClass": 1
  }
}
View input parameters
{ "csvData": "x1,x2,y\n0,1,0\n1,1,0\n1,2,0\n2,2,1\n3,2,1\n3,3,1\n4,3,1", "hasHeaderRow": true, "predictionValues": "2.5, 2.5", "learningRate": 0.2, "iterations": 3000, "threshold": 0.5, "decimalPlaces": 4 }

Key Facts

Category
Math, Date & Finance
Input Types
textarea, checkbox, text, number
Output Type
json
Sample Coverage
4
API Ready
Yes

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.

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.

Examples

1. Predicting Student Pass/Fail Status

Academic Researcher
Background
A researcher has data on student study hours and previous test scores and wants to predict future outcomes.
Problem
Determine if a student will pass (1) or fail (0) based on 2.5 hours of study and a score of 2.5.
How to Use
Paste the CSV data into the input field, set the prediction values to '2.5, 2.5', and run the calculation.
Example Config
learningRate: 0.2, iterations: 3000, threshold: 0.5
Outcome
The model returns an accuracy of 1.0 and predicts the student will pass (Class 1).

2. Marketing Conversion Analysis

Digital Marketer
Background
A marketer wants to know if a website visitor will convert based on time spent on site and number of pages visited.
Problem
Train a model on historical conversion data to find the probability of a new lead converting.
How to Use
Upload the CSV containing 'TimeOnSite, PagesVisited, Converted' and adjust the iterations for better convergence.
Example Config
learningRate: 0.1, iterations: 5000, threshold: 0.6
Outcome
The tool provides the model's accuracy and the specific probability of conversion for the input parameters.

Try with Samples

csv, hash
Duplicate Line Samples
Sample files with various types of duplicate lines for testing duplicate removal tools
preferred input family csv
csv
Regex Replace Samples
Collection of common and useful regex replacement patterns for text transformation and data cleaning
preferred input family csv
csv
Windows String Processing - C# Samples
Comprehensive C# string processing examples for Windows platform including string manipulation, splitting, joining, regex operations, and text analysis
preferred input family csv
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CSV Samples
Sample CSV files with various data types, sizes, and complexity levels
preferred input family csv
csv

Related Hubs

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Compare CSV to Excel, JSON, HTML, Markdown, XML, and text conversion tools in one hub for tabular export and interchange workflows.
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FAQ

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.

API Documentation

Request Endpoint

POST /en/api/tools/logistic-regression-calculator

Request Parameters

Parameter Name Type Required Description
csvData textarea No -
hasHeaderRow checkbox No -
predictionValues text No -
learningRate number No -
iterations number No -
threshold number No -
decimalPlaces number No -

Response Format

{
  "key": {...},
  "metadata": {
    "key": "value"
  },
  "error": "Error message (optional)",
  "message": "Notification message (optional)"
}
JSON Data: JSON Data

AI MCP Documentation

Add this tool to your MCP server configuration: 

{
  "mcpServers": {
    "elysiatools-logistic-regression-calculator": {
      "name": "logistic-regression-calculator",
      "description": "Fit a binary logistic regression model with gradient descent and predict class probabilities",
      "baseUrl": "https://elysiatools.com/mcp/sse?toolId=logistic-regression-calculator",
      "command": "",
      "args": [],
      "env": {},
      "isActive": true,
      "type": "sse"
    }
  }
}

You can chain multiple tools, e.g.: `https://elysiatools.com/mcp/sse?toolId=png-to-webp,jpg-to-webp,gif-to-webp`, max 20 tools.

If you encounter any issues, please contact us at [email protected]