When working with Scikit-learn, a popular machine learning library in Python, you may encounter the error, "Number of splits > number of samples." This error usually occurs when attempting to perform operations like cross-validation on a dataset that is either too small or incorrectly specified for the defined number of splits. In this article, we'll explore how to fix this error efficiently through different strategies.
Understanding the Error
The error message is straightforward: it indicates that the number of splits requested for the cross-validation test is greater than the number of samples in your dataset. Specifically, cross_val_score or KFold are the places where such an issue is frequently encountered.
from sklearn.model_selection import KFold, cross_val_score
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
# Load iris data
iris = load_iris()
X, y = iris.data, iris.target
# Instantiate Logistic Regression and KFold
model = LogisticRegression(max_iter=200)
kf = KFold(n_splits=150)In the example above, there are only 150 samples in the Iris dataset. If we attempt to create 150 splits, it means each split would require at least one sample, which is mathematically the boundary limit for this dataset.
Solutions to Fix the Error
1. Decrease the Number of Splits
One of the simplest and most direct methods to avoid this error is by reducing the number of splits to a number lower than or equal to the total number of available samples.
# Set a more realistic number of splits
kf = KFold(n_splits=5) #5 splits
scores = cross_val_score(model, X, y, cv=kf)
print("Cross-validation scores are:", scores)
With the number of splits set to 5, you allow each fold to use 150 / 5 = 30 samples.
2. Use StratifiedKFold for Better Class Distribution
While KFold divides samples into n_splits assumes random, equal division, StratifiedKFold is designed for classification tasks that can preserve class distribution within each fold.
from sklearn.model_selection import StratifiedKFold
# Use StratifiedKFold with 5 folds
skf = StratifiedKFold(n_splits=5)
scores = cross_val_score(model, X, y, cv=skf)
print("Stratified cross-validation scores are:", scores)
Using StratifiedKFold ensures that each fold is a good representative of your entire dataset with equal distribution of classes.
3. Increase the Number of Samples
If possible, expanding your dataset with more samples can universalize current limitations. This can be an ideal solution especially if the dataset holds seasonal data or is historically expansive over time.
Best Practices
While the methods above can quickly resolve this specific Sklearn error, consider following some best practices in your projects to prevent running into such issues in the future:
- Always inspect your dataset for its size and class balance before designing a cross-validation framework.
- Adjust the number of splits based on the data specifics. Aim for K in a split to ideally allow multiple complete rounds over data.
- Employ utility functions like
train_test_splitbeforehand to estimate the need for such splits.
By understanding potential pitfalls in your data-preprocessing or split strategy, you'll not only avoid this error but can also design more robust and precise machine learning models.
