CBSE Class 10th AI (Artificial Intelleigence) | 28. Introduction to Model Evaluation by Abraham | Learn Smarter
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28. Introduction to Model Evaluation

Model evaluation is a crucial phase in the AI life cycle that assesses how well machine learning models learn from data and make predictions. It is pivotal to check for accuracy, avoid overfitting, compare models, and improve performance. Techniques like hold-out validation and cross-validation, along with metrics such as accuracy, precision, recall, and F1 score, are essential for ensuring models are effective and reliable.

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Sections

  • 28

    Introduction To Model Evaluation

    Model evaluation is crucial to assess the performance of machine learning models, ensuring they make accurate predictions on new data.

    Learning Practice

  • 28.1

    Why Model Evaluation Is Important

    Model evaluation is crucial for assessing the performance, accuracy, and reliability of machine learning models.

    Learning Practice

  • 28.2

    Types Of Datasets Used

    This section explains the three primary types of datasets used in model training and evaluation: training set, validation set, and test set.

    Learning Practice

  • 28.3

    Evaluation Techniques

    This section introduces various techniques for evaluating machine learning models to ensure their effectiveness.

    Learning Practice

  • 28.3.1

    Hold-Out Validation

    Hold-Out Validation is a simple data splitting technique for evaluating machine learning models by separating data into training and testing sets.

    Learning Practice

  • 28.3.2

    K-Fold Cross-Validation

    K-Fold Cross-Validation is a technique that divides data into k equal parts to train and test machine learning models, providing a more reliable performance estimate.

    Learning Practice

  • 28.3.3

    Leave-One-Out Cross-Validation (Loocv)

    LOOCV is an evaluation technique in which each sample in the dataset is used once as a test set while the remaining samples form the training set, providing a high accuracy estimation.

    Learning Practice

  • 28.4

    Performance Metrics

    Performance metrics are essential for assessing the effectiveness of machine learning models.

    Learning Practice

  • 28.4.1

    Accuracy

    Accuracy is a fundamental performance metric in model evaluation, indicating the proportion of correct predictions made by a model.

    Learning Practice

  • 28.4.2

    Precision

    Precision is a performance metric that evaluates the accuracy of positive predictions made by a machine learning model.

    Learning Practice

  • 28.4.3

    Recall

    Recall is a performance metric that evaluates how well a model identifies all relevant instances from the positive class.

    Learning Practice

  • 28.4.4

    F1 Score

    The F1 Score is a performance metric in machine learning that balances precision and recall.

    Learning Practice

  • 28.4.5

    Confusion Matrix

    The confusion matrix is a tool that helps visualize the performance of a classification model by summarizing true positive, true negative, false positive, and false negative predictions.

    Learning Practice

  • 28.5

    Overfitting And Underfitting

    Overfitting occurs when a model excels on training data but fails on unseen data, while underfitting indicates a model's shortfall in capturing patterns.

    Learning Practice

  • 28.6

    Real-Life Example

    This section illustrates the importance of model evaluation by using a real-life example of a spam detection model.

    Learning Practice

References

Chapter_28_Intro.pdf

Class Notes

Memorization

What we have learnt

  • Model evaluation is essenti...
  • Data is split into training...
  • Techniques like hold-out va...

Final Test

Revision Tests