ML Metrics Visualization 3: Model Performance Radar Chart

Interactive Multi-Model Comparison

About this tool: Compare multiple ML models across key performance metrics. Add your models' scores to visualize strengths and weaknesses at a glance. Perfect for model selection and performance communication.

🤖 Quick Model Presets

📊 Add Custom Model

Model Name:

Accuracy (0-1):

Precision (0-1):

Recall (0-1):

F1 Score (0-1):

📋 Current Models

📚 Understanding ML Metrics

🎯 Accuracy

Overall correctness of predictions. Best for balanced datasets where all classes are equally important.

Accuracy = (TP + TN) / Total

Good when: Classes are balanced

Misleading when: Imbalanced datasets

🔍 Precision

Of all positive predictions, how many were actually correct. Critical when false positives are costly.

Precision = TP / (TP + FP)

Good when: False positives are expensive

Example: Medical diagnosis, spam detection

🎣 Recall

Of all actual positives, how many were correctly identified. Important when missing positives is costly.

Recall = TP / (TP + FN)

Good when: False negatives are expensive

Example: Disease screening, fraud detection

⚖️ F1 Score

Harmonic mean of precision and recall. Balances both metrics, especially useful for imbalanced datasets.

F1 = 2 × (Precision × Recall) / (Precision + Recall)

Good when: Need balance between precision and recall

Range: 0-1 (higher is better)

🔄 Precision vs Recall Trade-off

There's often a trade-off between precision and recall:

High Precision, Low Recall: Conservative model - few predictions, but very accurate
Low Precision, High Recall: Liberal model - many predictions, but less accurate
F1 Score: Helps balance this trade-off for optimal performance

💡 Choosing the Right Metric

Balanced datasets: Accuracy is usually sufficient
Imbalanced datasets: Focus on Precision, Recall, and F1 Score
Cost-sensitive: Choose based on the relative cost of false positives vs false negatives
Multi-class problems: Consider macro vs micro averaging