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What This Node Does

The Eval ML Model node evaluates trained machine learning model performance using comprehensive metrics and visualizations. Assess classification accuracy, regression error rates, generate confusion matrices, ROC curves, and compare multiple models to select the best performer for deployment. [SCREENSHOT: Eval ML Model node showing performance metrics and visualizations]

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When to Use This Node

Use the Eval ML Model node when you need to:
  • Assess model performance - Measure accuracy, error rates, and prediction quality
  • Compare multiple models - Evaluate Logistic Regression vs Random Forest vs XGBoost
  • Validate before deployment - Ensure model meets quality thresholds before production
  • Understand model behavior - Analyze confusion matrix, false positives/negatives

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Step-by-Step Usage Guide

1

Add Eval ML Model node

2

Connect trained model

Connect Build ML Model node output to Eval ML Model input[SCREENSHOT: Build ML Model connected to Eval ML Model]
3

Select metrics

For Classification: Choose Accuracy, Precision, Recall, F1-Score, AUC-ROC For Regression: Choose RMSE, MAE, R², MAPE[SCREENSHOT: Metrics selection panel]
4

Enable visualizations

Classification: Confusion Matrix, ROC Curve, Feature Importance Regression: Predicted vs Actual, Residual Plot[SCREENSHOT: Visualizations enabled]
5

Review results

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Tips and Best Practices

Always Evaluate Before Deployment: Never deploy models without comprehensive evaluation. Set minimum quality thresholds.
Use Multiple Metrics: Don’t rely on accuracy alone. For classification, check Precision, Recall, F1, and AUC-ROC together.
Confusion Matrix is Critical: Always review confusion matrix to understand error types (FP vs FN) and their business impact.
Compare Multiple Models: Train 2-3 algorithms and compare performance. Don’t assume first model is best.
Watch for Overfitting: Compare training accuracy vs test accuracy. Large gap (e.g., 99% train, 70% test) indicates overfitting.
Tune Threshold for Business Needs: Default 0.5 threshold may not be optimal. Tune based on cost of false positives vs false negatives.