logistic regression in r

Logistic regression is a powerful tool for binary classification problems in machine learning. In R, we can use the built-in glm function to perform logistic regression.


main.r
# Load the dataset
data <- read.csv("path/to/dataset.csv")

# Split the dataset into a training and testing set
training.set <- sample(nrow(data), nrow(data)/3*2)
train <- data[training.set,]
test <- data[-training.set,]

# Fit the logistic regression model
model <- glm(target_variable ~ predictor_variable1 + predictor_variable2, data = train, family = "binomial")

# Make predictions on the testing set
predictions <- predict(model, newdata = test, type = "response")

# Evaluate the performance of the model
threshold <- 0.5 # choose a threshold for classification
predicted.classes <- ifelse(predictions > threshold, 1, 0)
actual.classes <- test$target_variable
accuracy <- sum(predicted.classes == actual.classes) / length(actual.classes)
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In the above code, replace target_variable and predictor_variable1 and predictor_variable2 with the names of the target and predictor variables in your dataset. The family argument specifies that we want to perform binary logistic regression.

The glm function outputs a model object, which we can use to make predictions on new data using the predict function. We then use a threshold of 0.5 to classify the predictions as either 0 or 1, and compare those predictions to the actual classes in the testing set to calculate the accuracy of the model.

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