29. Model Evaluation Terminology
Evaluating the performance of AI models is crucial to ensure their accuracy and reliability. The chapter introduces key terminologies such as True Positive, False Negative, Precision, Recall, Accuracy, and others that assist in assessing model effectiveness. Understanding these concepts allows for better model improvement and performance evaluation.
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What we have learnt
- Model evaluation is essential for assessing AI model performance.
- Key metrics like Precision, Recall, and Accuracy provide insights into model effectiveness.
- Overfitting and underfitting are important considerations in model training.
Key Concepts
- -- True Positive (TP)
- The model predicted YES, and the actual answer was YES.
- -- True Negative (TN)
- The model predicted NO, and the actual answer was NO.
- -- False Positive (FP)
- The model predicted YES but the actual answer was NO.
- -- False Negative (FN)
- The model predicted NO but the actual answer was YES.
- -- Confusion Matrix
- A table used to describe the performance of a classification model showing TP, TN, FP, and FN.
- -- Accuracy
- Ratio of how often the model is correct, calculated as (TP + TN) / (TP + TN + FP + FN).
- -- Precision
- The ratio of correctly predicted YES cases to all predicted YES cases.
- -- Recall
- The ratio of correctly predicted YES cases to all actual YES cases.
- -- F1 Score
- A balance between Precision and Recall.
- -- Overfitting
- When a model performs well on training data but poorly on new data.
- -- Underfitting
- When a model performs poorly on both training and testing data.
- -- CrossValidation
- A technique to test how well a model performs by splitting the dataset into multiple parts.
- -- Bias
- Error arising from incorrect assumptions within the model.
- -- Variance
- Error due to excessive sensitivity to fluctuations in the training dataset.
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