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How do you select model after k-fold cross-validation?
Cross Validation is mainly used for the comparison of different models. For each model, you may get the average generalization error on the k validation sets. Then you will be able to choose the model with the lowest average generation error as your optimal model.
Is k-fold cross-validation A model validation technique?
That k-fold cross validation is a procedure used to estimate the skill of the model on new data. There are common tactics that you can use to select the value of k for your dataset. There are commonly used variations on cross-validation, such as stratified and repeated, that are available in scikit-learn.
How does k-fold cross validation affect model performance?
The estimate of model performance via k-fold cross-validation can be noisy. This means that each time the procedure is run, a different split of the dataset into k-folds can be implemented, and in turn, the distribution of performance scores can be different, resulting in a different mean estimate of model performance.
How to choose a predictive model after k-fold cross?
The differences in the observed performance are due to these two sources of variance. The “selection” you think about is a data set selection: selecting one of the surrogate models means selecting a subset of training samples and claiming that this subset of training samples leads to a superior model.
When to use repeated 10-fold cross validation?
For example, if 3 repeats of 10-fold cross-validation are used to estimate the model performance, this means that (3 * 10) or 30 different models would need to be fit and evaluated. Appropriate: for small datasets and simple models (e.g. linear).
How does cross validation work in model selection?
For cross validation to work as a model selection tool, you need approximate independence between the training and the test data. The problem with time series data is that adjacent data points are often highly dependent, so standard cross validation will fail.