OpenClaw LaunchOpenClaw Launch
🔢

Confusion Matrix Analyzer

Verified

by Community

Breaks down confusion matrix results into actionable insights. Calculates precision, recall, specificity, F1 score per class, and identifies where the model makes systematic errors for targeted improvement.

confusion-matrixclassificationanalysismetrics

Confusion Matrix Analyzer

Analyze confusion matrices to understand classifier performance and find improvement areas.

Usage

Provide your confusion matrix data to get detailed analysis and recommendations.

Examples

  • "Analyze this 3-class confusion matrix for our image classifier"
  • "Identify which classes our model confuses most often"
  • "Calculate per-class metrics from our binary classifier results"

Guidelines

  • Look at both false positives and false negatives
  • Calculate per-class precision and recall
  • Identify systematic confusion patterns between classes
  • Consider the business cost of each error type
  • Use normalized confusion matrices for imbalanced datasets

More Data Science & ML Skills

📓
Jupyter Notebook Organizer

Structure and organize Jupyter notebooks professionally.

🔧
Feature Engineering Guide

Create effective features for machine learning models.

📊
Model Evaluation Metrics

Choose and interpret the right ML evaluation metrics.

🎛️
Hyperparameter Tuning

Optimize model hyperparameters systematically.