Model Evaluation
ICSE · Class 12 · Artificial Intelligence
Summary of Model Evaluation for ICSE Class 12 Artificial Intelligence. Key concepts, important points, and chapter overview.
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Overview
Model evaluation is the process of checking how reliable an AI model is by comparing its outputs on test data with the actual answers. Separate testing data is used instead of training data because a model can simply memorize the training set, which leads to overfitting. The efficiency of a model is
Key Concepts
Evaluation is the process of understanding
Evaluation is the process of understanding the reliability of any AI model by feeding the test dataset into the model and comparing the results with a
Testing data is the separated part
Testing data is the separated part of the dataset used after training to judge how well the model performs.
Accuracy is the percentage of correct
Accuracy is the percentage of correct predictions out of all predictions. The formula is Accuracy = (Number of Correct Predictions) / (Total Number of
Error is the proportion of incorrect
Error is the proportion of incorrect predictions and is given by Error = 1 - Accuracy.
A True Positive means prediction
A True Positive means prediction is Yes and reality is Yes. A True Negative means prediction is No and reality is No.
Learning Objectives
- Understand the role of evaluation in AI model development
- Use Accuracy and Error to judge model performance
- Understand classification outcomes such as True Positive, True Negative, False Positive, and False Negative
- Interpret the confusion matrix as a record that supports evaluation
- Use Precision, Recall, and F1 Score to compare classification models
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