GITNUX MARKETDATA REPORT 2023
Must-Know Data Science Metrics
Highlights: The Most Important Data Science Metrics
- 1. Accuracy
- 2. F1-Score
- 3. Precision
- 4. Recall (Sensitivity)
- 5. Specificity
- 6. Balanced Accuracy
- 7. AUC-ROC (Area Under the Receiver Operating Characteristic curve)
- 8. Log-Loss (Logarithmic Loss)
- 9. Mean Absolute Error (MAE)
- 10. Mean Squared Error (MSE)
- 11. Root Mean Squared Error (RMSE)
- 12. R-squared (Coefficient of Determination)
- 13. Adjusted R-squared
- 14. Mean Absolute Percentage Error (MAPE)
- 15. Mean Squared Logarithmic Error (MSLE)
- 16. Median Absolute Deviation (MAD)
- 17. Confusion Matrix
- 18. Feature Importance
- 19. Lift
- 20. Kolmogorov-Smirnov Statistics (K-S)
Table of Contents
Data Science Metrics: Our Guide
As the world of data evolves, it’s imperative to understand key metrics that aid in interpreting and analysing data efficiently. Unveiling the power of vital data science metrics can lead you to actionable insights and data-driven decisions. Dive into our detailed blog post to discover must-know data science metrics that are reshaping the way businesses view their data landscape.
The proportion of correct predictions made by the model out of the total predictions. It is used to evaluate classification models.
The harmonic mean of precision and recall, ranging from 0 to 1. Fl-Score is used when both false positives and false negatives are important.
Measures the proportion of true positives out of the total predicted positives. High precision means a low false positive rate.
Measures the proportion of true positives out of the total actual positives. High recall means a low false negative rate.
Measures the proportion of true negatives out of the total actual negatives. It indicates the model’s ability to correctly identify negatives.
The average of sensitivity and specificity, used for imbalanced datasets where the positive and negative classes have different proportions.
AUC-ROC (Area Under Curve Receiver Operating Characteristic): 0-1 range, higher value signifies better classification.
A performance metric for evaluating the probability estimates of a classification model. It penalizes the model for both incorrect and uncertain predictions.
Mean Absolute Error
The average of the absolute differences between actual and predicted values in a regression model.
Mean Squared Error
The average of the squared differences between actual and predicted values in a regression model. Emphasizes larger errors.
Root Mean Squared Error
The square root of the mean squared error. Represents the standard deviation of the differences between predicted and actual values.
R-squared: 0-1 range, higher values mean better model predictability.
A modified version of the R-squared that adjusts for the number of predictors in the model.
Mean Absolute Percentage Error
The average of the absolute percentage errors between actual and predicted values in a regression model.
Mean Squared Logarithmic Error
The average of the squared logarithmic differences between actual and predicted values in a regression model. Emphasizes errors on smaller values.
Frequently Asked Questions
What are Data Science Metrics?
What are some common Data Science Metrics used in model evaluation?
How do Data Science Metrics help in improving the performance of data science projects?
Can Data Science Metrics be customized to evaluate specific goals or KPIs?
What is the importance of choosing the right Data Science Metrics in a project?
How we write these articles
We have not conducted any studies ourselves. Our article provides a summary of all the statistics and studies available at the time of writing. We are solely presenting a summary, not expressing our own opinion. We have collected all statistics within our internal database. In some cases, we use Artificial Intelligence for formulating the statistics. The articles are updated regularly. See our Editorial Guidelines.