Explore 12 Key Machine Learning Techniques in R

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Overview of Machine Learning in R:

R is a popular programming language and environment for statistical computing and graphics. It provides various packages and libraries for machine learning tasks. Some of the popular machine learning packages in R include:

1. caret: The “caret” package is a comprehensive framework for building machine learning models. It provides a unified interface for various algorithms.
2. randomForest: This package is specifically designed for building random forest models, which are an ensemble learning method.
3. glmnet: For generalized linear models and regularization techniques, the “glmnet” package is widely used.
4. e1071: It includes functions for support vector machines (SVM), a powerful algorithm for classification and regression tasks.
5. keras and tensorflow: For deep learning, you can use the “keras” package, which interfaces with the TensorFlow library.

Example Code Snippets:

1. Linear Regression with lm:

# Load the dataset
data(mtcars)

# Fit a linear regression model
model <- lm(mpg ~ wt + hp, data = mtcars)

# Print the summary
summary(model)