advanced-algorithmic-trading

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Figure 20.4: Test MSE curves for multiple training-validation splits for a linear regression with
polynomial features of increasing degree
training-test split. However, we need to use the KFold object to iterate over k "folds".
In particular the KFold object provides an iterator that allows us to correctly index the
samples in the data set and create separate training/test folds. I have chosen k = 10 for this
example.
As with the validation set approach we create a pipeline of polynomial feature transformations
and then apply a linear regression model. We then calculate the test MSE and construct separate
test MSE curves for each fold. Finally we create an average MSE curve across folds:
..
..
def k_fold_cross_val_poly(folds, degrees, X, y):
"""
Use the k-fold cross validation method to create
k separate training test splits over linear
regression models of varying flexibility
"""
# Create the KFold object and
# set the initial fold to zero
n = len(X)
kf = KFold(n, n_folds=folds)
kf_dict = dict(
[("fold_%s" % i,[]) for i in range(1, folds+1)]
)