Cross-validation is a common technique used in machine learning to evaluate the performance of a model. It involves splitting the dataset into multiple subsets or folds, training the model on some of the folds, and evaluating it on the remaining fold(s).

The fastai library provides a simple and efficient way to perform cross-validation in Python. In this blog post, we will explore different cross-validation strategies available in fastai and how to implement them.

K-Fold Cross-Validation

K-Fold Cross-Validation is a popular cross-validation strategy that partitions the dataset into K equally-sized folds. The model is trained K times, each time using K-1 folds for training and the remaining fold for validation. The final performance metric is obtained by averaging the results of each fold.

Here’s an example of how to perform K-Fold Cross-Validation using fastai:

from fastai.tabular.all import *
from fastai.cross_validation import *

# Load dataset
df = pd.read_csv('data.csv')

# Create TabularPandas object
procs = [Categorify, FillMissing, Normalize]
splits = RandomSplitter(valid_pct=0.2)(range_of(df))
to = TabularPandas(df, procs=procs, cont_names=cont_names, y_names=target_col, splits=splits)

# Define model
learn = tabular_learner(dls, metrics=accuracy)

# Perform cross-validation
n_folds = 5
cv_results = cross_validate(learn, to, cv=KFold(n_folds))

# Print cross-validation results
print(cv_results)

In this example, we first load the dataset and create a TabularPandas object using the desired preprocessing techniques. We then define the model and use the cross_validate function to perform K-Fold Cross-Validation with 5 folds. Finally, we print the cross-validation results.

Stratified K-Fold Cross-Validation

Stratified K-Fold Cross-Validation is useful when dealing with imbalanced datasets, where some classes may have significantly fewer samples than others. It ensures that each fold contains approximately the same distribution of class labels as the entire dataset. This can help prevent the model from being biased toward the majority class.

To use Stratified K-Fold Cross-Validation in fastai, you can simply replace KFold with StratifiedKFold:

# Perform stratified cross-validation
n_folds = 5
cv_results = cross_validate(learn, to, cv=StratifiedKFold(n_folds))

# Print cross-validation results
print(cv_results)

By using StratifiedKFold, the dataset will be stratified based on the target variable, ensuring that each fold maintains the same class distribution.

Conclusion

Cross-validation is a crucial technique for evaluating models, and fastai provides convenient tools to perform cross-validation in Python. In this blog post, we explored the K-Fold and Stratified K-Fold Cross-Validation strategies available in fastai and provided example code to illustrate their usage.

By using these cross-validation strategies, you can obtain more robust performance estimates for your models and make better decisions when it comes to model selection and hyperparameter tuning. fastai makes it easy to implement these strategies and take advantage of their benefits. Happy coding!

Note: Make sure to replace data.csv, cont_names, and target_col with the appropriate values for your dataset.