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Machine Learning - Cross Validation


On this page, W3schools.com collaborates with NYC Data Science Academy, to deliver digital training content to our students.


Cross Validation

When adjusting models we are aiming to increase overall model performance on unseen data. Hyperparameter tuning can lead to much better performance on test sets. However, optimizing parameters to the test set can lead information leakage causing the model to preform worse on unseen data. To correct for this we can perform cross validation.

To better understand CV, we will be performing different methods on the iris dataset. Let us first load in and separate the data.

from sklearn import datasets

X, y = datasets.load_iris(return_X_y=True)

There are many methods to cross validation, we will start by looking at k-fold cross validation.


K-Fold

The training data used in the model is split, into k number of smaller sets, to be used to validate the model. The model is then trained on k-1 folds of training set. The remaining fold is then used as a validation set to evaluate the model.

As we will be trying to classify different species of iris flowers we will need to import a classifier model, for this exercise we will be using a DecisionTreeClassifier. We will also need to import CV modules from sklearn.

from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import KFold, cross_val_score

With the data loaded we can now create and fit a model for evaluation.

clf = DecisionTreeClassifier(random_state=42)

Now let's evaluate our model and see how it performs on each k-fold.

k_folds = KFold(n_splits = 5)

scores = cross_val_score(clf, X, y, cv = k_folds)

It is also good pratice to see how CV performed overall by averaging the scores for all folds.

Example

Run k-fold CV:

from sklearn import datasets
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import KFold, cross_val_score

X, y = datasets.load_iris(return_X_y=True)

clf = DecisionTreeClassifier(random_state=42)

k_folds = KFold(n_splits = 5)

scores = cross_val_score(clf, X, y, cv = k_folds)

print("Cross Validation Scores: ", scores)
print("Average CV Score: ", scores.mean())
print("Number of CV Scores used in Average: ", len(scores))
Run example »

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Stratified K-Fold

In cases where classes are imbalanced we need a way to account for the imbalance in both the train and validation sets. To do so we can stratify the target classes, meaning that both sets will have an equal proportion of all classes.

Example

from sklearn import datasets
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import StratifiedKFold, cross_val_score

X, y = datasets.load_iris(return_X_y=True)

clf = DecisionTreeClassifier(random_state=42)

sk_folds = StratifiedKFold(n_splits = 5)

scores = cross_val_score(clf, X, y, cv = sk_folds)

print("Cross Validation Scores: ", scores)
print("Average CV Score: ", scores.mean())
print("Number of CV Scores used in Average: ", len(scores))
Run example »

While the number of folds is the same, the average CV increases from the basic k-fold when making sure there is stratified classes.


Leave-One-Out (LOO)

Instead of selecting the number of splits in the training data set like k-fold LeaveOneOut, utilize 1 observation to validate and n-1 observations to train. This method is an exaustive technique.

Example

Run LOO CV:

from sklearn import datasets
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import LeaveOneOut, cross_val_score

X, y = datasets.load_iris(return_X_y=True)

clf = DecisionTreeClassifier(random_state=42)

loo = LeaveOneOut()

scores = cross_val_score(clf, X, y, cv = loo)

print("Cross Validation Scores: ", scores)
print("Average CV Score: ", scores.mean())
print("Number of CV Scores used in Average: ", len(scores))
Run example »

We can observe that the number of cross validation scores performed is equal to the number of observations in the dataset. In this case there are 150 observations in the iris dataset.

The average CV score is 94%.


Leave-P-Out (LPO)

Leave-P-Out is simply a nuanced diffence to the Leave-One-Out idea, in that we can select the number of p to use in our validation set.

Example

Run LPO CV:

from sklearn import datasets
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import LeavePOut, cross_val_score

X, y = datasets.load_iris(return_X_y=True)

clf = DecisionTreeClassifier(random_state=42)

lpo = LeavePOut(p=2)

scores = cross_val_score(clf, X, y, cv = lpo)

print("Cross Validation Scores: ", scores)
print("Average CV Score: ", scores.mean())
print("Number of CV Scores used in Average: ", len(scores))
Run example »

As we can see this is an exhaustive method we many more scores being calculated than Leave-One-Out, even with a p = 2, yet it achieves roughly the same average CV score.


Shuffle Split

Unlike KFold, ShuffleSplit leaves out a percentage of the data, not to be used in the train or validation sets. To do so we must decide what the train and test sizes are, as well as the number of splits.

Example

Run Shuffle Split CV:

from sklearn import datasets
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import ShuffleSplit, cross_val_score

X, y = datasets.load_iris(return_X_y=True)

clf = DecisionTreeClassifier(random_state=42)

ss = ShuffleSplit(train_size=0.6, test_size=0.3, n_splits = 5)

scores = cross_val_score(clf, X, y, cv = ss)

print("Cross Validation Scores: ", scores)
print("Average CV Score: ", scores.mean())
print("Number of CV Scores used in Average: ", len(scores))
Run example »

Ending Notes

These are just a few of the CV methods that can be applied to models. There are many more cross validation classes, with most models having their own class. Check out sklearns cross validation for more CV options.