This is your AI metrics cheat sheet:
Classification Metrics: When sorting data into categories.
Precision: Out of all the items the model labeled as positive, how many truly are?
- Think of it as the model’s trustworthiness when it claims something is positive.Recall (Sensitivity): Out of all actual positive items, how many did the model correctly identify?
- It’s about not missing out on true positives.F1 Score: The balance between Precision and Recall.
- When you want a single metric that considers both false positives and false negatives.AUC-ROC: The model’s ability to distinguish between classes.
- A score of 1.0 is perfect. Above 0.9 is excellent. Below 0.5 means the model is worse than random guessing.
Regression Metrics: When predicting continuous values.
Mean Absolute Error (MAE): The average difference between predicted and actual values.
- Lower is better. If MAE is zero, be cautious: it could indicate overfitting or other issues.Root Mean Squared Error (RMSE): Like MAE, but punishes large errors more.
- Critical when significant mispredictions can have major consequences.R-squared: How well the model’s predictions match the real data.
- Ranges from 0 to 1. Closer to 1 means the model explains more of the variability.
Clustering Metrics: When you’re grouping data.
Silhouette Score: Measures how similar items are within a cluster compared to other clusters.
- Ranges from -1 to 1. Higher values indicate better-defined clusters.Davies-Bouldin Index: Lower values indicate better partitioning of clusters.
- Zero is the theoretical ideal score.
Data Quality Metrics:
Completeness: Percentage of non-missing data points.
- Aim for 100%, but be wary of artificially complete data.Consistency: Ensuring data doesn’t contradict itself.
- Inconsistent data can lead to misleading model results.Outlier Detection: Identify data points that deviate significantly.
- Outliers can skew model training.
Model Robustness:
Cross-Validation Score: A measure of a model’s performance on different subsets of data.
- Ensures the model isn’t just memorizing the training data.Bias-Variance Tradeoff: Balance between the model’s adaptability and its ability to generalize to new data.
- Ideally a model that captures patterns without overfitting or being overly simplistic.
Model Interpretability:
Feature Importance: Identifies the input variables that have the most influence on the model’s predictions.
- Understanding what the model deems important.SHAP Values: Breaks down the contribution of each feature to specific model predictions.
- Clarifies the reasoning behind individual predictions.
Overfitting & Underfitting: Ensuring the model performs well on both training and unseen data.
Training vs. Validation Error: A low training error coupled with a high validation error suggests potential overfitting.
Overfitting: Model performs well on training data but poorly on new data.
Underfitting: Model performs poorly on both training and new data.
This guide serves as a simple starting point for how to measure the effectiveness of different models!
