Azure AI Engineer Training | Microsoft Azure AI Engineer Training

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Bias and Variance in Machine Learning
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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.

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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.

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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.

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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).

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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).

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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.

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Bias-Variance Decomposition

• FormulaTotal Error Bias² Variance
  Irreducible Error
• Explanation Breaking down the components of
  model error.
• Graphical Representation Show how the error
  behaves with increasing model complexity.

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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.

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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 complexthey are
  the ones that generalize well to unseen data.

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CONTACT

• Azure AI - 102
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• Nilagiri Block, Aditya Enclave,
• Ameer pet, Hyderabad-1 
• Ph. No 91-9989971070 
• Visit www.visualpath.in 
• E-Mail online_at_visualpath.in

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