What does training error curve?
In a nutshell, a learning curve shows how error changes as the training set size increases. As we increase the training set size, the model cannot fit perfectly anymore the training set. So the training error becomes larger. However, the model is trained on more data, so it manages to fit better the validation set.
What is the cross-validation error?
Cross-Validation is a technique used in model selection to better estimate the test error of a predictive model. The idea behind cross-validation is to create a number of partitions of sample observations, known as the validation sets, from the training data set.
What is the purpose of cross validation in machine learning?
The goal of cross-validation is to estimate the test error associated with a statistical model or select the appropriate level of flexibility for a particular statistical method. Again, we can recall from the article on the bias-variance tradeoff that the training error associated with a model can vastly underestimate the test error of the model.
What do you need to know about validation curves?
Validation curve ¶ To validate a model we need a scoring function (see Metrics and scoring: quantifying the quality of predictions ), for example accuracy for classifiers.
How does k-fold cross validation improve validation?
K-fold cross-validation improves upon the validation set approach by dividing the n observations into k mutually exclusive, and approximately equally sized, subsets known as “folds”. The first fold becomes a validation set, while the remaining k − 1 folds (aggregated together) become the training set.
How is cross validation used in a forecasting model?
Firstly we will define cross-validation and then describe how it works. Secondly, we will construct a forecasting model using an equity index and then apply two cross-validation methods to this example: the validation set approach and k-fold cross-validation.