Azure AI Engineer Training - Microsoft Azure AI Engineer Training

1. Bias and Variance in Machine Learning

2. Bias and Variance in Machine Learning • Title: Bias and Variance in Machine Learning • Subtitle: Understanding Model Performance and ErrorInclude your name, date, or other relevant information.

3. Introduction • Definition of Bias: Bias refers to the error due to overly simplistic assumptions in the learning algorithm. • Definition of Variance: Variance refers to the error due to the model's sensitivity to small fluctuations in the training data. • Goal of Machine Learning: Minimize both bias and variance to achieve optimal performance.

4. Bias-Variance Trade-off • Explanation: Balancing bias and variance is key in building a good model. • High Bias: Leads to under fitting. • High Variance: Leads to overfitting. • Trade-off Illustration: Show a graph that visually explains the trade-off.

5. High Bias (Under fitting) • Characteristics: • Simple models (e.g., linear regression) • Misses important patterns in the data. • Results in high training and test errors. • Example: Visual representation of under fitting on a dataset (linear model on non-linear data).

6. High Variance (Overfitting) • Characteristics: • Complex models (e.g., deep neural networks). • Captures noise along with the signal. • Low training error but high test error. • Example: Visual representation of overfitting (model tightly hugging training data points).

7. Optimal Model (Balanced Bias and Variance) • Characteristics: • Strikes a balance between bias and variance. • Low training and test error. • Generalizes well to new data. • Example: Visual showing a model that fits the data appropriately.

8. Bias-Variance Decomposition • Formula:Total Error = Bias² + Variance + Irreducible Error • Explanation: Breaking down the components of model error. • Graphical Representation: Show how the error behaves with increasing model complexity.

9. Strategies to Handle Bias and Variance • Reduce Bias: • Use more complex models. • Increase model capacity (e.g., from linear regression to polynomial regression). • Reduce Variance: • Use techniques like cross-validation, regularization (L1/L2), and simplifying models. • Increase training data. • Practical Example: Briefly describe how these strategies work in real-world scenarios.

10. Conclusion • Key Takeaways: • Balancing bias and variance is critical for a well-performing model. • Understand the trade-off to avoid under fitting or overfitting. • Use appropriate techniques to optimize models. • Closing Thought: In machine learning, the best models aren't always the most complex—they are the ones that generalize well to unseen data.

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