Evaluation Metrics for Machine Learning Models

  • BeginnerLevel

  • 2 hrs 0 minsDuration

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About this Course

  • Learn key metrics for classification (Confusion Matrix, Accuracy, AUC-ROC, F-Score) and regression (MAE, MSE, R2 Score).
  • Reinforce learning with interactive quizzes on classification and regression evaluation techniques.
  • Understand how to measure and improve machine learning model performance on unseen data.

Learning Outcomes

Evaluation Metrics

Learn how evaluation metrics improve ML model performance.

Classification Assessment

Learn key classification metrics like Accuracy, Precision-Recall.

Regression Evaluation

Master regression metrics such as MAE, MSE, RMSE, and R².

Model Performance

Learn to choose the right evaluation metrics for various scenarios.

Course Curriculum

Explore a comprehensive curriculum covering Python, machine learning models, deep learning techniques, and AI applications.

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  1. 1. Types of Machine Learning

  2. 2. Why do we need Evaluation Metrics?

  3. 3. AI&ML Blackbelt Plus Program (Sponsored)

  1. 1. Confusion Matrix

  2. 2. Quiz: Confusion Matrix

  3. 3. What is Accuracy

  4. 4. Quiz: Accuracy

  5. 5. Alternatives of Accuracy

  6. 6. Quiz: Alternatives of Accuracy

  7. 7. Precision and Recall

  8. 8. Quiz: Precision and Recall

  9. 9. What is F-Score

  10. 10. Thresholding

  11. 11. What is AUC-ROC

  12. 12. Quiz: AUC-ROC

  13. 13. What is Log Loss

  14. 14. Quiz: Log Loss

  15. 15. Gini Coefficient

  1. 1. MAE and MSE

  2. 2. RMSE and RMSLE

  3. 3. Quiz: RMSE and RMSLE

  4. 4. R2 and Adjusted R2

  5. 5. R2 and Adjusted R2

  1. 1. Cross-Validation

  2. 2. The Way Forward

Meet the instructor

Our instructor and mentors carry years of experience in data industry

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Kunal Jain

Founder & CEO, Analytics Vidhya

Kunal has 15+ years of experience in the field of Data Science and is the founder and CEO of Analytics Vidhya- the world's 2nd largest Data Science community.

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With this course you’ll get

  • 120 hour

    Duration

  • Kunal Jain

    Instructor

  • Beginner

    Level

Certificate of completion

Earn a professional certificate upon course completion

  • Globally recognized certificate
  • Verifiable online credential
  • Enhances professional credibility

Frequently Asked Questions

Looking for answers to other questions?

Evaluation metrics assess machine learning model performance, guiding improvements for better accuracy.

Evaluation metrics are categorized into classification (Accuracy, Precision-Recall, F1-Score, AUC-ROC, Log Loss) and regression (MAE, MSE, RMSE, R²) to assess and improve model performance.

Yes! Since DeepSeek is open-source, you can freely use it in commercial projects while also customizing it to fit your needs.

Evaluation metrics, as you might have guessed by now, will be of supreme importance in machine learning hackathons.

Cross Validation is one of the most important concepts in any type of machine learning model and a data scientist should be well versed in how it works.

Yes, you will receive a certificate of completion after successfully finishing the course and assessments.

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