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VOOZH | about |
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VOOZH | about |
The caret (Classification And Regression Training) package in R provides a unified framework for training, tuning and evaluating a wide range of machine learning algorithms.
We will install caret and load it along with any other necessary dependencies.
We will be using the iris data set which is a built-in dataset in R Language. We will load the data and set a random seed and use createDataPartition() to split into 80% training and 20% testing to evaluate performance on unseen observations.
There are many classification algorithms available in Caret package. We will define cross-validation, implement the model and evaluate the model on test data.
Random Forest aggregates many decision trees to reduce overfitting and improve accuracy.
Output:
CART builds a single tree and prunes it based on the complexity parameter (cp).
Output:
k-NN classifies observations based on the majority vote of their k nearest neighbors.
Output:
SVM separates classes by finding the hyperplane with maximum margin.
Output:
There are many classification algorithms available in Caret package. We will define cross-validation and summarise the model and evaluate the model using Root mean squared error (RMSE).
We’ll use the mtcars dataset to demonstrate regression examples.
Ordinary least squares provides a baseline for regression.
Output:
Random Forest Regression is an ensemble method that builds multiple decision trees and combines their results to improve accuracy and reduce overfitting.
Output:
In this article, we demonstrated how to train and evaluate different classification and regression algorithms using the caret package in R, providing a consistent framework for model building, tuning and comparison.
