Binary Prediction Qualifiers

Easily modify prediction endpoints with /binary to transform the predictions for categorical analysis.

Each prediction type (e.g., Grid, Max Fit, Partial-Sample Regression) offers a binary qualifier endpoint, designed specifically for categorical analysis.

How to Use the Binary Qualifier

For example, to perform a grid prediction suitable for categorical analysis, append /binary to the grid endpoint, as shown below:

https://api.csanalytics.io/v2/prediction-engine/grid/binary

Purpose

These binary endpoints transform predictions to produce binary outcomes (e.g., probability of success/failure, yes/no) instead of magnitude outcomes. This setup is ideal for situations where predictions are categorized, enabling seamless compatibility with binary and logistic models such as logit.

Example Use Case: If a prediction model calculates the likelihood of a particular sports outcome, the binary endpoint converts this likelihood into a categorical result (e.g., win/lose), making it more suitable for analysis within binary classification frameworks.

Configuration Consistency

All configurations and parameters for each prediction method endpoint remain the same. To transform a prediction for categorical analysis, simply append the /binary qualifier to the endpoint—no additional changes to parameters are required.