Machine Learning Evaluation Methods
Machine Learning Product Evaluation Rubric
Category 1: Model Performance
Accuracy: The degree to which the machine learning model accurately predicts the target variable.
Precision: The degree to which the model correctly predicts positive instances.
Recall: The degree to which the model correctly identifies all relevant positive instances.
F1 Score: The harmonic mean of precision and recall, taking into account false positives and false negatives.
AUC-ROC Score: The measure of the model's ability to distinguish between positive and negative instances.
Category 2: Model Interpretability
Explainability: The degree to which the machine learning model is interpretable and can provide insights into how it makes predictions.
Feature Importance: The degree to which the model can identify the most important features in making predictions.
Model Complexity: The degree to which the model is complex and difficult to interpret.
Category 3: Data Management
Data Quality: The degree to which the data used to train the machine learning model is clean, accurate, and representative.
Data Quantity: The amount of data used to train the machine learning model and whether it is sufficient to achieve the desired level of performance.
Data Privacy: The degree to which the data used to train the machine learning model is kept private and secure.
Category 4: Technical Implementation
Scalability: The degree to which the machine learning model can handle large amounts of data and high traffic volumes.
Latency: The time it takes for the machine learning model to generate a prediction.
Robustness: The degree to which the machine learning model can handle unexpected scenarios and edge cases.
Deployment: The ease and efficiency with which the machine learning model can be deployed to production.
Category 5: Ethical Considerations
Bias: The degree to which the machine learning model is biased towards or against certain groups.
Fairness: The degree to which the machine learning model treats all individuals and groups fairly.
Transparency: The degree to which the machine learning model's decision-making process is transparent and can be audited.
Accountability: The degree to which the machine learning model is accountable for its decisions and actions.
Category 6: User Experience
User Interface: The design and functionality of the user interface through which users interact with the machine learning product.
Performance: The speed and efficiency of the machine learning product in delivering results to users.
Accuracy: The degree to which the machine learning product delivers accurate and relevant results to users.
Reliability: The degree to which the machine learning product is reliable and consistent in its performance.
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