Jason's Notebook

HomeData Science
Data Science

Switch book

  • Knowledge Vault
  • Artificial Intelligence
  • Computer Science Introduction
  • Computer Science Theory
  • Computer Systems
  • Data Science, selected book
  • Development Environment
  • Machine Learning Systems
  • Mathematics
  • Physics
  • Robotics
  • Transformers & Large Language Models

Explorer

  • Data Visualization
    • Summarizing and Visualizing Data
    • Summarizing and Visualizing Data in Python
  • Drawing Insights
    • Data Analysis
    • Descriptive Statistics and Analytics
    • Descriptive Statistics Lab
    • Probability Distributions
  • Model Evaluation
    • Association Rule Mining Metrics
      • Lift Metric
      • Support & Confidence
    • Classification Metrics
      • Accuracy
      • Brier Score
      • Cohen Kappa
      • Confusion Matrix
      • F1-Score
      • Matthews Correlation Coefficient (MCC)
      • Precision
      • Precision-Recall (PR) AUC
      • Recall
      • Receiver Operating Characteristic (ROC) AUC
    • Clustering Metrics
      • Calinski-Harabasz Index
      • Davies-Bouldin Index
      • Dunn Index
      • Elbow Method
      • Gap Statistics
      • Silhouette Score
    • Image & Dimensionality Reduction Metrics
      • Peak Signal-to-Noise Ratio (PSNR)
      • Reconstruction Error
    • Natural Language Processing Metrics
      • BLEU Score
      • Perplexity
    • Physics-Informed & Scientific Metrics
      • Energy Conservation Error
      • Physics-Informed Loss
      • Relative Error
      • Residual Loss
      • Structural Similarity Index (SSIM)
    • Regression Metrics
      • Adjusted R-Squared (Adjusted R²)
      • Mean Absolute Error (MAE)
      • Mean Absolute Percentage Error (MAPE)
      • Mean Squared Error (MSE)
      • R-Squared (R²)
      • Root Mean Squared Error (RMSE)
  • Regression Analysis
    • Regression Model Evaluation
    • Regression Models
    • Regression Technique Comparison
    • Testing Regression Models
    • Training Regression Models
  • Sampling
    • Non-Probability Sampling
    • Probability Sampling
  • Statistical Inference
    • Correlation Analysis
    • Types of Statistical Inference
  • What is Data Science
    • Data Science Career Path
    • Data Science Lifecycle
    • Data Sources
    • Measurement Types
    • Types of Data
Home

❯

Data Science

❯

Model Evaluation

❯

Regression Metrics

Regression Metrics

Aug 06, 20261 min read

INFO

This folder contains evaluation metrics for regression models.
These metrics assess prediction accuracy, error magnitude, and model fit.
Use this index to navigate by error type or interpretability.

Error-Based Metrics

  • Mean Absolute Error (MAE)
  • Mean Squared Error (MSE)
  • Root Mean Squared Error (RMSE)
  • Mean Absolute Percentage Error (MAPE)

Fit-Based Metrics

  • R-Squared (R²)
  • Adjusted R-Squared (Adjusted R²)

6 items under this folder.

  • Aug 06, 2026

    Adjusted R-Squared (Adjusted R²)

    • Aug 06, 2026

      Mean Absolute Error (MAE)

      • Aug 06, 2026

        Mean Absolute Percentage Error (MAPE)

        • Aug 06, 2026

          Mean Squared Error (MSE)

          • Aug 06, 2026

            R-Squared (R²)

            • Aug 06, 2026

              Root Mean Squared Error (RMSE)


              Recent Notes

              • Adders & Subtractors

                Sep 07, 2026

                • computer-systems
                • digital-systems
                • alu
                • adders
                • subtractors
                • arithmetic-circuits
              • Comparator

                Sep 07, 2026

                • computer-systems
                • digital-systems
                • alu
                • comparators
                • arithmetic-circuits

              Backlinks

              • Model Evaluation

              Graph View

              • Error-Based Metrics
              • Fit-Based Metrics

              Created with Quartz v5.0.0 © 2026

              • GitHub
              • Portfolio