Machine Learning Basics | Chapter 8: Model Evaluation Metrics by Prakhar Chauhan | Learn Smarter
K12 Students

Academics

AI-Powered learning for Grades 8–12, aligned with major Indian and international curricula.

Academics

Grades

Grade 8 Grade 9 Grade 10 Grade 11 Grade 12

Curriculum

CBSE ICSE IB
Professionals

Professional Courses

Industry-relevant training in Business, Technology, and Design to help professionals and graduates upskill for real-world careers.

Professional Courses
Games

Interactive Games

Fun, engaging games to boost memory, math fluency, typing speed, and English skills—perfect for learners of all ages.

games
Typing
Memory
Math
English Adventures
Knowledge
Chapter 8: Model Evaluation Metrics

Evaluation metrics are crucial for assessing the performance of classification models. Various metrics such as confusion matrix, accuracy, precision, recall, F1 score, and ROC curve provide insights into a model's effectiveness, especially in cases where data may be imbalanced. Understanding and applying these metrics ensures a comprehensive evaluation beyond just basic accuracy.

Enroll to start learning

You’ve not yet enrolled in this course. Please enroll for free to listen to audio lessons, classroom podcasts and take mock test.

Sections

  • 8

    Model Evaluation Metrics

    This section discusses various metrics used to evaluate the performance of classification models.

    Learning Practice

  • 8.1

    Why Model Evaluation Is Important

    Model evaluation is crucial for determining the reliability and effectiveness of machine learning models, as mere accuracy can be misleading, especially in imbalanced datasets.

    Learning Practice

  • 8.2

    Confusion Matrix

    The Confusion Matrix serves as a powerful tool to evaluate the performance of classification models, detailing the outcomes of predictions.

    Learning Practice

  • 8.3

    Accuracy

    Accuracy measures the ratio of correctly predicted observations to the total observations, offering a snapshot of model performance.

    Learning Practice

  • 8.4

    Precision

    Precision is a critical metric in classification that measures the accuracy of positive predictions made by the model.

    Learning Practice

  • 8.5

    Recall (Sensitivity)

    Recall, also known as sensitivity, measures the percentage of actual positives that were correctly predicted by a classification model.

    Learning Practice

  • 8.6

    F1 Score

    The F1 Score is a critical evaluation metric in classification that provides a balance between precision and recall.

    Learning Practice

  • 8.7

    Roc Curve And Auc

    The ROC Curve and AUC are crucial tools for evaluating the performance of classification models by visualizing the trade-off between true positive and false positive rates.

    Learning Practice

  • 8.8

    Summary Table

    This section summarizes key model evaluation metrics that are essential for understanding classification model performance.

    Learning Practice

References

Untitled document (40).pdf

Class Notes

Memorization

What we have learnt

  • The importance of diverse e...
  • How to construct and interp...
  • The definitions and applica...

Final Test

Revision Tests