This web app provides a comprehensive and interactive way to analyze the performance of binary classification models using ROC and precision-recall curves, along with class separation and confusion matrices.
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Uploading and Processing Data
Upload Data: Click on the Upload Data File (CSV) button and select a CSV file from your computer. The file should contain two columns for actual and predicted values.
Generate Curves: After uploading the data, click the Generate Curves button. This will process the uploaded data and generate the ROC curve, precision-recall curve, class separation histogram, and confusion matrix.
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Generating Random Data and Plotting It
Enter Sample Size: Input a desired sample size in the Enter Sample Size field. The default is set to 1,000.
Generate Random Data: Click on Generate Random Data & Curves to create random data based on the provided sample size and plot the respective curves and matrix.
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Downloading Data
Download Data as CSV: After generating curves using either your own data or random data, you can download the results by clicking the Download Data as CSV button. This will download a file containing the actuals and predictions used in the curves.
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Understanding the Display
ROC Curve: This graph plots the True Positive Rate (TPR) against the False Positive Rate (FPR), providing insights into the performance of the binary classifier.
Precision-Recall Curve: This graph shows the trade-off between precision and recall for different thresholds.
Class Separation Histogram: These histograms display the distribution of scores for the positive and negative classes, helping visualize class separation.
Confusion Matrix: Presented as a heatmap, it illustrates the number of true positives, true negatives, false positives, and false negatives.
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Navigating the Interface
Interactivity: You can hover over points on the curves to see exact values at different thresholds.
Visual Aids: Annotations on the graphs will indicate key metrics like AUC (Area Under the Curve) for the ROC curve and AP (Average Precision) for the precision-recall curve.
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Additional Features
Customizable Sample Size for Random Data: You can modify the sample size for generating random data to see how different sample sizes affect the curves and matrix.
Error Handling: The app will alert you if you attempt to generate curves without uploading data or entering a valid sample size.