K-Fold Cross Validation Technique and its Essentials

Introduction

Welcome to this comprehensive guide on model evaluation and selection techniques in machine learning, particularly focusing on K-fold cross-validation and its application in time series analysis. Before delving into the specifics, let’s consider the importance of these techniques in monitoring model performance before deployment. Understanding the performance metrics such as mean squared error, which evaluates the deviation between predicted and observed values, is crucial in ensuring model accuracy. We will explore how K-fold cross-validation, especially in the context of time series data, helps in training and validating models using multiple train-test splits.

By employing K-fold cross-validation, with features like test_index and train_index, we can mitigate overfitting and understand how the model generalizes to unseen data. Furthermore, we will examine the role of neural networks in classification tasks, highlighting their application in subsamples and their ability to learn complex patterns. Join us on this journey to optimize your machine learning models and enhance their performance.

In this article, you will understand the importance of k-fold cross-validation in machine learning. We’ll explore how k-fold cross validation, including 5-fold and 10-fold cross validation, enhances model reliability and reduces overfitting through effective data partitioning.

Learning Outcomes

• Understand the concept of n_splits in 5 fold cross validation in machine learning cross-validation and implement K-fold cross-validation with different values of n_splits.
• Discuss how the choice of n_splits affects the model evaluation.
• Explain the significance of random_state in machine learning models.
• Discuss how setting random_state ensures reproducibility of results.
• Implement random_state in scikit-learn for various classifiers and regression models.
• Implement various machine learning algorithms using scikit-learn.
• Understand the importance of stratified k-fold cross-validation in classification problems.
• Discuss the advantages and limitations of train-test split compared to other validation techniques.
• Implement various classifiers (e.g., SVM, Random Forest, Logistic Regression) using scikit-learn.
• Discuss strategies for handling new data in machine learning models.

This article was published as a part of the Data Science Blogathon.

Understanding Model Performance in Cross Validation

Machine learning model performance assessment is just like assessing the scores, how we used to evaluate our sores in high schools and colleges for the meeting the eligibility criteria for getting the best courses or getting selected in the campus interviews for companies for the job and clearing cut-off scores for many more competition exams for getting selected. So apparently, the GOOD score recognizes the fact that the candidate is always good. The same is been expected in the machine learning model, and that should achieve the expected results in predictions/forecasting/calcification problem statements. Even in the ML world, the model has been trained in the context of data, model, and code.

What is K-Fold Cross Validation?

K-fold cross validation in machine learning cross-validation is a powerful technique for evaluating predictive models in data science. It involves splitting the dataset into k subsets or folds, where each fold is used as the validation set in turn while the remaining k-1 folds are used for training. This process is repeated k times, and performance metrics such as accuracy, precision, and recall are computed for each fold. By averaging these metrics, we obtain an estimate of the model’s generalization performance. This method is essential for model assessment, selection, and hyperparameter tuning, offering a reliable measure of a model’s effectiveness. Compared to leave-one-out cross-validation, which uses k equal to the number of samples, K-fold cross-validation is computationally efficient and widely used in practice.

In each set (fold) training and the test would be performed precisely once during this entire process. It helps us to avoid overfitting. As we know when a model is trained using all of the data in a single short and give the best performance accuracy. To resist this k fold cross validation in machine learning cross-validation helps us to build the model is a generalized one.

To achieve this K-Fold Cross Validation, we have to split the data set into three sets, Training, Testing, and Validation, with the challenge of the volume of the data.

Here Test and Train data set will support building model and hyperparameter assessments.

In which the model has been validated multiple times based on the value assigned as a parameter and which is called K and it should be an INTEGER.

Make it simple, based on the K value, the data set would be divided, and train/testing will be conducted in a sequence way equal to K time.

Life Cycle of K-Fold Cross-Validation

       Image designed by the author

Let’s have a generalised K value. If K=5, it means, in the given dataset and we are splitting into 5 folds and running the Train and Test. During each run, one fold is considered for testing and the rest will be for training and moving on with iterations, the below pictorial representation would give you an idea of the flow of the fold-defined size.

In which each data point is used, once in the hold-out set and K-1 in Training. So, during the full iteration at least once, one fold will be used for testing and the rest for training.

In the above set, 5- Testing 20 Training. In each iteration, we will get an accuracy score and have to sum them and find the mean. Here we can understand how the data is spread in a way of consistency and will make a conclusion whether to for the production with this model (or) NOT.

Parameter Tuning with K-Fold

Let us consider the RandomForestClassifier for this analysis, and n_estimators is our parameter for this case and CV as 10 (commonly used)

scores1 = cross_val_score(RandomForestClassifier(n_estimators=5),digits.data, digits.target, cv=10)

print("Avg Score for Estimators=5 and CV=10 :",np.average(scores1))

Output

Avg Score for Estimators=5 and CV=10 : 0.87369

scores2 = cross_val_score(RandomForestClassifier(n_estimators=20),digits.data, digits.target, cv=10)

print("Avg Score for Estimators=20 and CV=10 :",np.average(scores2))

Output

Avg Score for Estimators=20 and CV=10 : 0.93377

scores3 = cross_val_score(RandomForestClassifier(n_estimators=30),digits.data, digits.target, cv=10)

print("Avg Score for Estimators=30 and CV=10 :",np.average(scores3))

Output

Avg Score for Estimators=30 and CV=10 : 0.94879

scores4 = cross_val_score(RandomForestClassifier(n_estimators=40),digits.data, digits.target, cv=10)

print("Avg Score for Estimators=40 and CV=10 :",np.average(scores4))

Output

Avg Score for Estimators=40 and CV=10 : 0.94824

Based on the above observation, we will go with Estimators=30.

Thumb Rules Associated with K Fold

Now, we will discuss a few thumb rules while playing with K – fold

• K should be always >= 2 and = to number of records, (LOOCV)
  • If 2 then just 2 iterations
  • If K=No of records in the dataset, then 1 for testing and n- for training
• The optimized value for the K is 10 and used with the data of good size. (Commonly used)
• If the K value is too large, then this will lead to less variance across the training set and limit the model currency difference across the iterations.
• The number of folds is indirectly proportional to the size of the data set, which means, if the dataset size is too small, the number of folds can increase.
• Larger values of K eventually increase the running time of the cross-validation process.

Please remember K-Fold Cross Validation for the below purpose in the ML stream.

• Model selection
• Parameter tuning
• Feature selection

So far, we have discussed the K Fold and its way of implementation, let’s do some hands-on now.

What is Cross Validation in Machine Learning?

Cross-validation is a method to evaluate the performance of a model on data it has not been trained on. It’s basically a method to determine if your model is too fixated on the particular training data it received (overfitting) and would struggle with unfamiliar data.

This is how it operates:

• Divide the data: Your data set is separated into various subsets, commonly known as folds.
• Train the model on all folds except the one left out for both training and testing. Next, you evaluate how well the model performs on the fold that was not included in training.
• Redo and assess: You repeat this procedure for every fold, essentially teaching the model several times with varying data. In conclusion, you take the average of the outcomes from every test to obtain a stronger estimation of the model’s performance on new data.

Practical Example With Code

I am creating a simple array, defining the K size as 5 and splitting my array. Using the simple loop and printing the Train and Test portions. Here we could see clearly that the data points in TT buckets and Test data are unique in each cycle.

from sklearn.model_selection import KFold

import numpy as np

data=np.array([5,10,15,20,25,30,35,40,45,50,55,60,65,70,75,80,85,90,95,100])
kfold=KFold(5,shuffle = True)
for train,test in kfold.split(data):
  print("Train: %s,Test: %s"%(data[train],data[test]))

Model Selection using K-Fold

from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier
import numpy as np
from sklearn.datasets import load_digits
import matplotlib.pyplot as plt
digits = load_digits()
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(digits.data,digits.target,test_size=0.3)

imported required libraries and loaded digits (hand-written digits – open source), let’s apply a different algorithm.

Logistic Regression

I am using liblinear. This is the “Large Linear Classification” category. It uses a Coordinate-Descent Algorithm. This would minimize a multivariate function by resolving the univariate and its optimization problems during the loop.

lr = LogisticRegression(solver='liblinear',multi_class='ovr')
lr.fit(X_train, y_train)
lr.score(X_test, y_test)

Output

Score : 0.972222

SVC

Just using gamma is a parameter for non-linear perspective for hyperplanes. The value of the gamma tries to fit the training data set and uses 1/n_features.

svm = SVC(gamma='auto')
svm.fit(X_train, y_train)
svm.score(X_test, y_test)

Output

Score : 0.62037

Random Forest

For RFC, I am assigning estimators as 40.

rf = RandomForestClassifier(n_estimators=40)
rf.fit(X_train, y_train)
rf.score(X_test, y_test)

Output

Score: 0.96666

Scores from the above list of algorithms Logistic Regression and Random Forest are doing comparatively better than SVM.

Now will use cross_val_score function and get the scores, passing different algorithms with dataset and cv.

from sklearn.model_selection import cross_val_score

Set LogisticRegression, CV =3

score_lr=cross_val_score(LogisticRegression(solver='liblinear',multi_class='ovr'), digits.data, digits.target,cv=3)
print(score_lr)
print("Avg :",np.average(score_lr))

Output: for 3 fold we have 3 scores

[0.89482471 0.95325543 0.90984975]
Avg : 0.9193099610461881

Set SVM and CV=3

score_svm =cross_val_score(SVC(gamma='auto'), digits.data, digits.target,cv=3)

print(score_svm)

print("Avg :",np.average(score_svm))

Output: Scores

[0.38063439 0.41068447 0.51252087]
Avg : 0.4346132442960489

Set Random Forest and CV=3

score_rf=cross_val_score(RandomForestClassifier(n_estimators=40),digits.data, digits.target,cv=3)

print(score_rf)

print("Avg :",np.average(score_rf))

Output: Scores

[0.92821369 0.95325543 0.92320534]
Avg : 0.9348914858096827

Based on the above table, we will go with Random Forest for this dataset for production. But we have to monitor the model performance based on the data drift and as the business case changes, we have to revisit the model and redeploy.

K-Fold in Visual form

Visual representation is always the best evidence for any data which is located across the axes.

from sklearn.model_selection import cross_val_score

knn = KNeighborsClassifier(n_neighbors=5)
scores = cross_val_score(knn, X, y, cv=10, scoring='accuracy')
print(scores.mean())

Output

0.9666666666666668

k_range = list(range(1, 25))
k_scores = []
for k in k_range:
    knn = KNeighborsClassifier(n_neighbors=k)
    scores = cross_val_score(knn, X, y, cv=10, scoring='accuracy')
    k_scores.append(scores.mean())
print(k_scores)

Output

[0.96, 0.95333, 0.96666, 0.96666, 0.966668, 0.96666, 0.966666, 0.966666, 0.97333, 0.96666, 0.96666, 0.97333, 0.9800, 0.97333, 0.97333, 0.97333, 0.97333, 0.98000, 0.9733333, 0.980000, 0.966666, 0.96666, 0.973333, 0.96, 0.96666, 0.96, 0.96666, 0.953333, 0.95333, 0.95333]

import matplotlib.pyplot as plt
%matplotlib inline
plt.plot(k_range, k_scores)
plt.xlabel('Value of K for KNN')
plt.ylabel('Cross-Validated-Accuracy')

Output: With a simple plot, X=> value of K and Y=> Accuracy for respective CV

The above visual representation helps us to understand the accuracy is ~98%for K=12,18 and 19 for KNN.

ML Engineers and Business Team Agreement

As we know, there are various methods to evaluate model performance. It is our team’s responsibility to construct a robust and generalized model that meets production expectations. Additionally, we need to effectively communicate its performance and the business benefits to stakeholders and customers, guided by SMEs, to achieve our goals.

As we are an ML engineer team, we must provide the performance of the model in the numeric range. Let’s say the performance of the model would be 85-90%. Sometimes the performance of the model in training and testing will not behave the same in production, in many cases, Overfitting or Underfitting will be experienced during the production environment.

Yes! Of course, this is really threatening to junior Data scientists and ML Engineers, but the challenge is one requires to improvise your technical capabilities, right? , So after many iterations and CI/CD involvement (MLOps), only the model will achieve the accuracy as expected and in a generalised mode. One step further, always we have to monitor the performance and apply the necessary changes to the model algorithm and code.

Will see how we could overcome this in the real-time, scenario.

As I mentioned earlier the RANGE-Factor, we have different techniques to evaluate, in which Cross-Validation or 5 fold cross validation is best and easy to understand. This is simple in nature and involves a typical resampling technique, without any replacement in the data. And easily we could understand and visualise while implementing.

Conclusion

Employing K fold cross validation enables a comprehensive evaluation of model performance by partitioning the entire dataset into K equal-sized subsets. This method allows us to mitigate the impact of imbalanced data and provides reliable cross-validation results for deep learning models. By selecting the appropriate hyperparameters based on these results, we can optimize model performance and enhance its generalization ability across the entire dataset.

Hope you like the article! K-fold cross-validation is a powerful technique in machine learning that enhances model evaluation. By dividing data into k subsets, such as 5-fold or 10-fold cross-validation, it ensures robust performance assessment.

Key Takeaways

• The test dataset is crucial for evaluating the performance of a trained model on unseen data, ensuring it generalizes well beyond the training set.
• After training a model on the training data, it’s essential to evaluate its performance on both the validation and test datasets to ensure it meets performance expectations.
• Validation data helps in tuning model hyperparameters and assessing the model’s performance before finalizing it for deployment.
• The KFold class from the sklearn.model_selection module is instrumental in splitting the data into K folds for cross-validation, ensuring robust model evaluation and preventing overfitting.

Frequently Asked Questions

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Shanthababu Pandian 03 Sep, 2024

Shanthababu has 20+ years of experience in Information Technology (IT). Expertise in ML & Data Architect and Program Delivery. Analytics and AIML Solutions Lead, Data Architecting, Modelling, Data Quality, Data Governance, Data Privacy and Data Integration, Data Platform, Data analysis and Data Science program.