CBSE Class 10th AI (Artificial Intelleigence) | 29. Model Evaluation Terminology by Abraham | Learn Smarter
K12 Students

Academics

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

Professionals

Professional Courses

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

Games

Interactive Games

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

Typing
Memory
Math
English Adventures
Knowledge
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.

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 practice test.

Sections

  • 29

    Model Evaluation Terminology

    Model evaluation terminology is essential for assessing the performance of AI models to ensure accurate predictions and reliable outcomes.

    Learning Practice

  • 29.1

    What Is Model Evaluation?

    Model evaluation is the process of measuring how well an AI model performs on given data in order to assess its prediction accuracy and reliability.

    Learning Practice

  • 29.2

    Important Model Evaluation Terminologies

    Important model evaluation terminologies are crucial for understanding the performance of AI models by defining key concepts such as True Positives, False Negatives, Precision, and Recall.

    Learning Practice

  • 29.2.1

    True Positive (Tp)

    True Positive (TP) refers to instances where a model correctly predicts the positive class.

    Learning Practice

  • 29.2.2

    True Negative (Tn)

    True Negative (TN) is a metric indicating correct negative predictions in machine learning models.

    Learning Practice

  • 29.2.3

    False Positive (Fp) (Type I Error)

    False Positive (FP), also known as Type I Error, occurs when a model predicts a positive outcome that is incorrect.

    Learning Practice

  • 29.2.4

    False Negative (Fn) (Type Ii Error)

    False Negative (FN) refers to instances where a model predicts NO while the actual answer is YES, resulting in a missed opportunity for a correct diagnosis.

    Learning Practice

  • 29.3

    Confusion Matrix

    A confusion matrix is a table that summarizes the performance of a classification model by showing the counts of true positives, true negatives, false positives, and false negatives.

    Learning Practice

  • 29.4

    Accuracy

    Accuracy measures the overall correctness of a model's predictions.

    Learning Practice

  • 29.5

    Precision

    Precision measures the accuracy of the positive predictions made by a model.

    Learning Practice

  • 29.6

    Recall (Sensitivity Or True Positive Rate)

    Recall measures the proportion of actual positive cases that a model correctly predicts.

    Learning Practice

  • 29.7

    F1 Score

    The F1 Score is a crucial metric that balances precision and recall, offering a single score that captures the performance of a classification model.

    Learning Practice

  • 29.8

    Overfitting And Underfitting

    Overfitting occurs when a model performs well on training data but poorly on new data, while underfitting happens when the model fails to capture the underlying trend of the data.

    Learning Practice

  • 29.8.1

    Overfitting

    Overfitting refers to a model that performs well on training data but poorly on unseen data.

    Learning Practice

  • 29.8.2

    Underfitting

    Underfitting occurs when a model fails to learn enough from the training data, leading to poor performance on both training and testing datasets.

    Learning Practice

  • 29.9

    Cross-Validation

    Cross-validation is a technique used to evaluate the performance of a model by training and testing it on different subsets of the data.

    Learning Practice

  • 29.10

    Bias And Variance

    This section discusses bias and variance, two critical components affecting model performance in machine learning.

    Learning Practice

  • 29.10.1

    Bias

    Bias refers to the error that occurs due to incorrect assumptions in a model, often leading to underfitting.

    Learning Practice

  • 29.10.2

    Variance

    This section explores the concept of variance in machine learning, detailing how it relates to model performance and the balance with bias.

    Learning Practice

References

Chapter_29_Model.pdf

Class Notes

Memorization

What we have learnt

  • Model evaluation is essenti...
  • Key metrics like Precision,...
  • Overfitting and underfittin...

Final Test

Revision Tests