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Bias vs. Variance
Machine Learning
Franco Cedillo
Digital Product Manager, tech researcher
iOS Provider at Thought Recap S...
Diagnosing bias vs. variance
¿El problema es bias o variance?
Cross validation set
Learning Curves
Caso Redes Neuronales
Regularized Linear Regression
Error Cost:
What should we try next?
Get more training examples
Try smaller sets of features
Try getting additional features
Try addin...
Split the data in two portions
Errors
Bias y Variance de acuerdo al grado del polinomio
Bias y variance de acuerdo al parámetro de
regularización ƛ
Objetivo
How to systematically improve our learning algorithm?
When our algorithm is doing poorly?
How to debug our learni...
Learning Curves
High Bias
High Variance
Actions
Action Effect
Get more training examples Fixes high variance
Try smaller sets of features Fixes high variance
Try ...
Ejemplos en MATLAB
Recursos Extra
Anotaciones de la lección
http://www.holehouse.org/mlclass/10_Advice_for_applying_machine_learning.html
Lec...
Diapositivas de Apoyo
Training Set / c.v. Set / Test Set
60% / 30% / 30%
Hich bias
High Variance
Bias vs Variance
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Bias vs Variance

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Charla acerca de Bias vs Variance, basada en el tema expuesto por Andrew Ng en el curso Machine Learning de Stanford University, by Coursera

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Bias vs Variance

  1. 1. Bias vs. Variance Machine Learning Franco Cedillo Digital Product Manager, tech researcher iOS Provider at Thought Recap SFO past: PM Digital at La República, Ing. Informático PUCP
  2. 2. Diagnosing bias vs. variance ¿El problema es bias o variance? Cross validation set Learning Curves Caso Redes Neuronales
  3. 3. Regularized Linear Regression Error Cost:
  4. 4. What should we try next? Get more training examples Try smaller sets of features Try getting additional features Try adding polynomial features Try decreasing ƛ Try increasing ƛ
  5. 5. Split the data in two portions
  6. 6. Errors
  7. 7. Bias y Variance de acuerdo al grado del polinomio
  8. 8. Bias y variance de acuerdo al parámetro de regularización ƛ
  9. 9. Objetivo How to systematically improve our learning algorithm? When our algorithm is doing poorly? How to debug our learning algorithm?
  10. 10. Learning Curves
  11. 11. High Bias
  12. 12. High Variance
  13. 13. Actions Action Effect Get more training examples Fixes high variance Try smaller sets of features Fixes high variance Try getting additional features Fixes high bias Try adding polynomial features Fixes high bias Try decreasing ƛ Fixes high bias Try increasing ƛ Fixes high variance
  14. 14. Ejemplos en MATLAB
  15. 15. Recursos Extra Anotaciones de la lección http://www.holehouse.org/mlclass/10_Advice_for_applying_machine_learning.html Lección de la semana 6 en ML at Coursera Andrew Ng https://www.coursera.org/learn/machine-learning/home/week/6
  16. 16. Diapositivas de Apoyo
  17. 17. Training Set / c.v. Set / Test Set 60% / 30% / 30%
  18. 18. Hich bias
  19. 19. High Variance

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