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  • Introduction to Fiddler
    • Monitor, Analyze, and Protect your ML Models and Gen AI Applications
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    • Getting Started With Fiddler Guardrails
    • Getting Started with LLM Monitoring
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      • Selecting Enrichments
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      • Alerts
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      • Class Imbalanced Data
      • Enhance ML and LLM Insights with Custom Metrics
      • Data Drift: Monitor Model Performance Changes with Fiddler's Insights
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    • Enhance Model Insights with Fiddler's Slice and Explain
      • Events Table in RCA
      • Feature Analytics Creation
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      • Performance Charts Creation
      • Performance Charts Visualization
    • Master AI Monitoring: Create, Customize, and Compare Dashboards
      • Creating Dashboards
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    • Python Client API Reference
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  • History
    • Release Notes
    • Python Client History
    • Compatibility Matrix
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On this page
  • Performance Charts Visualizations for ML and LLM Models
  • Binary Classification
  • Multi-class Classification
  • Regression

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  1. Product Guide
  2. Enhance Model Insights with Fiddler's Slice and Explain

Performance Charts Visualization

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Last updated 2 months ago

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Performance Charts Visualizations for ML and LLM Models

List of possible performance visualization depending on the model task. To see how to create a Performance chart, visit .

Binary Classification

Confusion Matrix

A 2x2 table that shows how many predicted and actual values exist for positive and negative classes. Also referred as an error matrix. The percentage is computed per row.

Receiver Operating Characteristic (ROC) Curve

A graph showing the performance of a classification model at different classification thresholds. Plots the true positive rate (TPR), also known as recall, against the false positive rate (FPR).

Precision-Recall Curve

A graph that plots the precision against the recall for different classification thresholds.

Calibration Plot

A graph that tell us how well the model is calibrated. The plot is obtained by dividing the predictions into 10 quantile buckets (0-10th percentile, 10-20th percentile, etc.). The average predicted probability is plotted against the true observed probability for that set of points.

Multi-class Classification

Confusion Matrix

A table that shows how many predicted and actual values exist for different classes. Also referred as an error matrix. The percentage is computed per row (using all classes). In the full view, up to 15 classes can be displayed. In the chart creation mode, up to 12 classes can be displayed. The displayed labels can be controlled in the chart.

Regression

Prediction Scatterplot

Plots the predicted values against the actual values. The more closely the plot hugs the y=x line, the better the fit of the model.

Error Distribution

A histogram showing the distribution of errors (differences between model predictions and actuals). The closer to 0 the errors are, the better the fit of the model.

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