CBSE Class 12th AI (Artificial Intelligence) | 12. Evaluation Methodologies of AI Models by Abraham | Learn Smarter
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12. Evaluation Methodologies of AI Models

Evaluating AI models is crucial for understanding their performance in real-world scenarios, including checking predictions, error rates, and ensuring fairness. Various methodologies such as confusion matrices, evaluation metrics, cross-validation, and ROC curves provide frameworks to assess model quality. These techniques not only help in selecting the best-performing models but also address issues of bias and fairness in AI applications.

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Sections

  • 12

    Evaluation Methodologies Of Ai Models

    This section discusses the necessity of evaluating AI models, outlining various methodologies including the confusion matrix, evaluation metrics, and techniques like cross-validation.

    Learning Practice

  • 12.1

    Need For Evaluation

    The need for evaluation in AI model development is crucial to ensure accurate performance and reliability in real-world scenarios.

    Learning Practice

  • 12.2

    Confusion Matrix

    The confusion matrix is a tool used to evaluate the performance of classification models by comparing actual and predicted values.

    Learning Practice

  • 12.3

    Evaluation Metrics

    This section discusses key evaluation metrics derived from a confusion matrix to assess AI model performance.

    Learning Practice

  • 12.3.1

    Accuracy

    Accuracy measures the overall correctness of an AI model's predictions, but can be misleading in imbalanced datasets.

    Learning Practice

  • 12.3.2

    Recall (Sensitivity)

    Recall, also known as sensitivity, measures how effectively a model identifies actual positives among the total positives.

    Learning Practice

  • 12.3.3

    F1 Score

    The F1 Score is a metric that balances precision and recall, making it crucial for evaluating the performance of AI models, especially in scenarios where both false positives and false negatives carry significant importance.

    Learning Practice

  • 12.3.4

    Specificity

    Specificity measures how well an AI model identifies negative cases, ensuring reliability in performance.

    Learning Practice

  • 12.4

    Cross-Validation

    Cross-Validation involves splitting data into multiple parts to assess the performance of AI models in a more reliable way.

    Learning Practice

  • 12.5

    Train-Test Split

    The Train-Test Split methodology divides a dataset into two distinct parts for training and testing AI models, enabling evaluation of their performance.

    Learning Practice

  • 12.6

    Overfitting And Underfitting

    This section discusses overfitting and underfitting, two critical concepts in AI model evaluation that impact model performance on training and unseen data.

    Learning Practice

  • 12.7

    Roc Curve And Auc

    The ROC Curve and AUC are crucial tools for evaluating the performance of classification models, helping to optimize threshold values.

    Learning Practice

  • 12.8

    Comparing Ai Models

    This section discusses the methodology for comparing various AI models using consistent metrics and contextual considerations.

    Learning Practice

  • 12.9

    Bias And Fairness In Evaluation

    This section addresses the inherent bias that can affect AI models and emphasizes the importance of ensuring fairness during evaluation.

    Learning Practice

  • 12.10

    Tools For Evaluation

    This section discusses various tools available for evaluating AI models, specifically highlighting Scikit-learn, TensorFlow/Keras, and Google Colab/Jupyter.

    Learning Practice

References

Chapter_12_Evalu.pdf

Class Notes

Memorization

What we have learnt

  • Evaluation of AI models is ...
  • Metrics such as accuracy, p...
  • Understanding overfitting a...

Final Test

Revision Tests