overfitting faq

README.md

@@ -125,6 +125,7 @@ Bonus Notebooks (not in the book)
 
 ##### Model evaluation
 
+- [What is overfitting?](./overfitting.md)
 - [Is it always better to have the largest possible number of folds when performing cross validation?](./faq/number-of-kfolds.md)
 - [When training an SVM classifier, is it better to have a large or small number of support vectors?](./faq/num-support-vectors.md)
 - [How do I evaluate a model?](./faq/evaluate-a-model.md)

faq/README.md

@@ -63,12 +63,14 @@ Sebastian
 
 ##### Model evaluation
 
+- [What is overfitting?](./overfitting.md)
 - [Is it always better to have the largest possible number of folds when performing cross validation?](./number-of-kfolds.md)
 - [When training an SVM classifier, is it better to have a large or small number of support vectors?](./num-support-vectors.md)
 - [How do I evaluate a model?](./evaluate-a-model.md)
 - [What is the best validation metric for multi-class classification?](./multiclass-metric.md)
 - [What factors should I consider when choosing a predictive model technique?](./choosing-technique.md)
 
+
 ##### Logistic Regression
 
 - [What is the probabilistic interpretation of regularized logistic regression?](./probablistic-logistic-regression.md)

faq/overfitting.md

@@ -0,0 +1,24 @@
+# What is overfitting?
+
+Let’s assume we have a hypothesis or model m that we fit on our training data. In machine learning, the training performance — for example, accuracy — is what we measure and optimize during training time. Let’s call this training accuracy ACC<sub>train</sub>(*m*).
+
+Now, what we really care about in machine learning is to build a model that generalizes well to unseen data, that is, we want to build a model that has a high accuracy on the whole distribution of data; let’s call this ACC<sub>population</sub>(*m*). (Typically, we use cross-validation techniques and a separate, independent test set to estimate the  generalization performance.)
+
+Now, overfitting occurs if there’s an alternative model *m'*  from the algorithm's hypothesis space where the training accuracy is better and the generalization performance is worse compared to model *m* -- we say that m overfits the training data.
+
+### Learning Curves
+
+As a rule of thumb, a model is more likely to overfit if it is too complex given a fixed number of training samples. The figure below shows the training and validation accuracies of a SVM model on a certain dataset. Here, I plotted the accuracy as a function of the inverse regularization parameter C -- the larger the value of C the larger the penalty term against complexity.
+
+![](./overfitting/learning_curve_1.png)
+
+We observe a larger difference between training and test accuracy for increasing values of C (more complex models). Based on the plot, we can say that the model at < 10^-1 underfit the training data whereas the models > 10^-1 overfit the training data.
+
+### Remedies
+
+Remedies against overfitting include
+
+1. Choose a simpler model by adding bias and/or reducing the number of parameters
+	2. Adding regularization penalties
+	3. Reducing the dimensionality of the feature space
+2. Collecting more training data