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Artificial intelligence

Unit 1

Generative AI Course

Introduction to Generative AI

Key Techniques and Models in Generative AI

Applications of Generative AI in Art and Content Creation

Generative AI in Business and Industry

Ethical Implications of Generative AI

Unit 2

Machine Learning Course

Introduction to Machine Learning Concepts

Difference Between Supervised and Unsupervised Learning

Overview of Key Machine Learning Algorithms

How to Evaluate Machine Learning Models

Practical Applications of Machine Learning

Unit 3

Large Language Models Course

Introduction to Large Language Models (LLMs)

Training and Fine-tuning Large Language Models

Applications of LLMs in Natural Language Processing

Challenges and Limitations of LLM Technology

Future Trends in Large Language Models

Unit 2 • Chapter 4

How to Evaluate Machine Learning Models

Summary

Training a machine learning model requires evaluation to determine its real-world performance. Effective evaluation metrics vary based on the problem type. For classification tasks, accuracy is a popular metric due to its simplicity, representing the ratio of correctly predicted instances to the total instances. However, accuracy can be misleading, so more detailed metrics like precision and recall are also essential. Precision indicates the proportion of true positive instances among all instances labeled as positive, while recall measures the proportion of true positive instances among all actual positives. Both metrics provide different insights; precision focuses on the accuracy of positive labels, while recall emphasizes the capture ability of positive instances. These metrics can also extend beyond binary classification tasks, accommodating more complex classification scenarios.

Concept Check

What metric is defined as true positives over predicted positives?

This metric is called accuracy.

This metric is called F1 score.

This metric is called precision.

This metric is called recall.

What defines recall in machine learning evaluation?

Recall is true negatives over false positives.

Recall is true positives over actual positives.

Recall is false negatives over actual positives.

Recall is true positives over predicted positives.

Why might you not rely solely on accuracy?

Accuracy considers false positives only.

Accuracy is affected by class imbalance.

Accuracy is only used for regression tasks.

Accuracy may oversimplify evaluation in some cases.

Which metric compares models easily with one number?

The metric is recall.

The metric is specificity.

The metric is precision.

The metric is accuracy.

What does a high precision indicate?

High precision indicates few true positives.

High precision indicates few false positives.

High precision indicates model overfitting.

High precision indicates high recall.

For binary classification, how is precision calculated?

Precision is true negatives divided by false positives.

Precision is false positives divided by true positives.

Precision is true positives divided by actual positives.

Precision is true positives divided by predicted positives.

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