Bias vs Variance
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This document discusses diagnosing and addressing bias and variance problems in machine learning models. It provides examples of high bias versus high variance, including learning curves. It recommends actions like getting more training data to address high variance, or trying additional features to address high bias. These include splitting data into training, cross validation, and test sets, and tuning regularization parameters. Examples are provided in MATLAB and additional resources on bias-variance and machine learning are referenced.
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Bias vs Variance
- 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. Diagnosing bias vs. variance ¿El problema es bias o variance? Cross validation set Learning Curves Caso Redes Neuronales
- 3. Regularized Linear Regression Error Cost:
- 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. Split the data in two portions
- 6. Errors
- 7. Bias y Variance de acuerdo al grado del polinomio
- 8. Bias y variance de acuerdo al parámetro de regularización ƛ
- 9. Objetivo How to systematically improve our learning algorithm? When our algorithm is doing poorly? How to debug our learning algorithm?
- 10. Learning Curves
- 11. High Bias
- 12. High Variance
- 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
- 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
- 17. Training Set / c.v. Set / Test Set 60% / 30% / 30%
- 19. Hich bias
- 20. High Variance
Notes de l'éditeur
- Para una exposición clara vamos a tomar el caso de la Regresión Lineal
- x1, x2, x3, x4, ... (x1)^2, (x2)^2, x1.x2, ...
- Contexto: Es un trabajo, estudio, tesis Fecha límite
- Con más complejidad la el aprendizaje sobre la data de entrenamiento se vuelve más precisa. Así también se llega a acertar para el set de test, sin embargo con mayor complejidad se va perdiendo ese acierto.
- Algunos resultados están mal Alguien del equipo, un consultor, tal vez por parte del cliente obtiene gráficas que muestran un error grande Alguien ve el código e intuye un error No va a hacer nuestra tarea de implementar bien el algoritmo