SVM with Cross Validation in R
Last Updated :
11 Jul, 2024
Support Vector Machine (SVM) is a powerful and versatile machine learning model used for classification and regression tasks. In this article, we'll go through the steps to implement an SVM with cross-validation in R using the caret package.
Cross Validation in R
Cross-validation involves splitting the data into multiple parts (folds), training the model on some parts, and testing it on the remaining parts. The most common type is k-fold cross-validation, where the data is divided into k subsets (folds), and the model is trained k times, each time leaving out one of the folds for validation and using the rest for training.
Now we will discuss a Step-by-Step Guide for performing SVM with Cross Validation in R Programming Language.
Step 1: Install and Load Necessary Packages
Ensure you have the caret, e1071, and ggplot2 packages installed. These packages provide the tools for creating and evaluating SVM models.
R
install.packages("caret")
install.packages("e1071")
install.packages("ggplot2")
library(caret)
library(e1071)
library(ggplot2)
Step 2: Load and Prepare the Data
For this example, we'll use the built-in iris dataset.
R
Output:
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3.0 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
4 4.6 3.1 1.5 0.2 setosa
5 5.0 3.6 1.4 0.2 setosa
6 5.4 3.9 1.7 0.4 setosa
Step 3: Set Up Cross-Validation Control
Define the control function for cross-validation using the trainControl function.
R
custom_control <- trainControl(
method = "cv", # Cross-validation
number = 5, # Number of folds
verboseIter = TRUE, # Output the progress
savePredictions = "final",
classProbs = TRUE, # Estimate class probabilities
summaryFunction = multiClassSummary # Use for multi-class classification
)
Step 4: Train and Evaluate SVM Model
Use the train function to train the SVM model with cross-validation. Print the summary of the trained model to see the results of cross-validation.
R
set.seed(123) # Set seed for reproducibility
svm_model <- train(
Species ~ ., # Formula for the model
data = iris, # Data to be used
method = "svmRadial", # SVM with radial basis function kernel
trControl = custom_control,
tuneLength = 10 # Tune over 10 different parameter values
)
print(svm_model)
Output:
Support Vector Machines with Radial Basis Function Kernel
150 samples
4 predictor
3 classes: 'setosa', 'versicolor', 'virginica'
No pre-processing
Resampling: Cross-Validated (5 fold)
Summary of sample sizes: 120, 120, 120, 120, 120
Resampling results across tuning parameters:
C logLoss AUC prAUC Accuracy Kappa Mean_F1 Mean_Sensitivity
0.25 0.1436566 0.9973333 0.8950640 0.9466667 0.92 0.9463806 0.9466667
0.50 0.1353177 0.9976667 0.8956340 0.9466667 0.92 0.9463806 0.9466667
1.00 0.1338090 0.9966667 0.8937293 0.9533333 0.93 0.9534371 0.9533333
2.00 0.1363814 0.9950000 0.8900847 0.9533333 0.93 0.9534371 0.9533333
4.00 0.1325292 0.9950000 0.8896610 0.9533333 0.93 0.9535384 0.9533333
8.00 0.1500452 0.9933333 0.8867857 0.9533333 0.93 0.9531318 0.9533333
16.00 0.1773016 0.9896667 0.8771895 0.9466667 0.92 0.9463472 0.9466667
32.00 0.1792608 0.9893333 0.8772480 0.9533333 0.93 0.9531318 0.9533333
64.00 0.1829900 0.9853333 0.8752865 0.9600000 0.94 0.9601883 0.9600000
128.00 0.1872609 0.9883333 0.8742261 0.9533333 0.93 0.9535384 0.9533333
Mean_Specificity Mean_Pos_Pred_Value Mean_Neg_Pred_Value Mean_Precision Mean_Recall
0.9733333 0.9517172 0.9747042 0.9517172 0.9466667
0.9733333 0.9517172 0.9747042 0.9517172 0.9466667
0.9766667 0.9583838 0.9777489 0.9583838 0.9533333
0.9766667 0.9583838 0.9777489 0.9583838 0.9533333
0.9766667 0.9573737 0.9774603 0.9573737 0.9533333
0.9766667 0.9579798 0.9779076 0.9579798 0.9533333
0.9733333 0.9529293 0.9750216 0.9529293 0.9466667
0.9766667 0.9579798 0.9779076 0.9579798 0.9533333
0.9800000 0.9646465 0.9809524 0.9646465 0.9600000
0.9766667 0.9573737 0.9774603 0.9573737 0.9533333
Mean_Detection_Rate Mean_Balanced_Accuracy
0.3155556 0.960
0.3155556 0.960
0.3177778 0.965
0.3177778 0.965
0.3177778 0.965
0.3177778 0.965
0.3155556 0.960
0.3177778 0.965
0.3200000 0.970
0.3177778 0.965
Tuning parameter 'sigma' was held constant at a value of 0.7668493
Accuracy was used to select the optimal model using the largest value.
The final values used for the model were sigma = 0.7668493 and C = 64.
Step 5: Plot the Results
Visualize the results using ggplot2.
R
Output:
SVM with Cross Validation in RUsing cross-validation, you can reliably estimate the performance of your SVM model and tune its parameters to achieve the best results. This approach ensures that your model generalizes well to unseen data.
Conclusion
Cross-validation is an essential technique in machine learning for assessing model performance and ensuring generalizability to unseen data. The caret package in R provides a comprehensive framework for implementing various cross-validation methods with ease. By following the steps outlined, you can effectively train and evaluate models using cross-validation, choose the best model parameters, and visualize the results for better understanding and interpretation.
Similar Reads
Cross validation in R without caret package
Cross-validation is a technique for evaluating the performance of a machine learning model by training it on a subset of the data and evaluating it on the remaining data. It is a useful method for estimating the performance of a model when you don't have a separate test set, or when you want to get
4 min read
Cross Validation in Machine Learning
In machine learning, simply fitting a model on training data doesn't guarantee its accuracy on real-world data. To ensure that your machine learning model generalizes well and isn't overfitting, it's crucial to use effective evaluation techniques. One such technique is cross-validation, which helps
8 min read
Time Series Cross-Validation
In this article, we delve into the concept of Time Series Cross-Validation (TSCV), a powerful technique for robust model evaluation in time series analysis. We'll explore its significance, implementation, and best practices, along with providing insightful code examples for clarity. What is Cross Va
4 min read
Cross Validation on a Dataset with Factors in R
Cross-validation is a widely used technique in machine learning and statistical modeling to assess how well a model generalizes to new data. When working with datasets containing factors (categorical variables), it's essential to handle them appropriately during cross-validation to ensure unbiased p
4 min read
Cross-Validation Using K-Fold With Scikit-Learn
Cross-validation involves repeatedly splitting data into training and testing sets to evaluate the performance of a machine-learning model. One of the most commonly used cross-validation techniques is K-Fold Cross-Validation. In this article, we will explore the implementation of K-Fold Cross-Valida
12 min read
How to perform 10 fold cross validation with LibSVM in R?
Support Vector Machines (SVM) are a powerful tool for classification and regression tasks. LibSVM is a widely used library that implements SVM, and it can be accessed in R with the e1071 package. Cross-validation, particularly 10-fold cross-validation, is an essential technique for assessing the per
5 min read
K- Fold Cross Validation in Machine Learning
K-Fold Cross Validation is a statistical technique to measure the performance of a machine learning model by dividing the dataset into K subsets of equal size (folds). The model is trained on K − 1 folds and tested on the last fold. This process is repeated K times, with each fold being used as the
4 min read
Stratified K Fold Cross Validation
Stratified K-Fold Cross Validation is a technique used for evaluating a model. It is particularly useful for classification problems in which the class labels are not evenly distributed i.e data is imbalanced. It is a enhanced version of K-Fold Cross Validation. Key difference is that it uses strati
4 min read
Cross-Validation vs. Bootstrapping
When developing machine learning models, it's crucial to assess their performance accurately. Cross-validation and bootstrapping are two fundamental techniques used for this purpose. They help estimate how well a model will generalize to an independent dataset, thereby guiding decisions on model sel
7 min read
Recursive Feature Elimination with Cross-Validation in Scikit Learn
In this article, we will earn how to implement recursive feature elimination with cross-validation using scikit learn package in Python. What is Recursive Feature Elimination (RFE)? Recursive Feature Elimination (RFE) is a feature selection algorithm that is used to select a subset of the most relev
5 min read