Implementing Binary Logistic Regression in R is straightforward using the built-in functions and libraries. Here is a step-by-step guide to implementing Binary Logistic Regression in R:
- Load the necessary libraries:
scsslibrary(stats)
library(glm)
- Load the data:
arduinodata <- read.csv("data.csv", header = TRUE)
- Split the data into training and testing datasets:
scssset.seed(123)
train_index <- sample(1:nrow(data), round(0.7 * nrow(data)), replace = FALSE)
train_data <- data[train_index,]
test_data <- data[-train_index,]
- Fit the Binary Logistic Regression model using the
glm()
function:
bashmodel <- glm(formula = outcome ~ predictor1 + predictor2 + predictor3, family = binomial(link = "logit"), data = train_data)
- Make predictions on the testing dataset using the
predict()
function:
bashpredictions <- predict(model, newdata = test_data, type = "response")
- Evaluate the model's performance using evaluation metrics such as accuracy, precision, recall, and F1 score:
scssactual <- test_data$outcome
predicted <- ifelse(predictions > 0.5, 1, 0)
accuracy <- sum(predicted == actual) / length(actual)
precision <- sum(predicted == 1 & actual == 1) / sum(predicted == 1)
recall <- sum(predicted == 1 & actual == 1) / sum(actual == 1)
f1_score <- 2 * (precision * recall) / (precision + recall)
- Visualize the results using plots such as a ROC curve and a confusion matrix:
scsslibrary(pROC)
roc <- roc(test_data$outcome, predictions)
plot(roc)
confusionMatrix(predicted, actual)
Note that in Step 4, the formula
argument specifies the model formula, where the outcome variable is on the left-hand side of the tilde (~) and the independent variables are on the right-hand side, separated by a plus (+) sign. The family
argument specifies the type of model to be fitted, in this case, binomial
for Binary Logistic Regression with logit
link function.
Also note that in Step 5, the type
argument in the predict()
function specifies the type of prediction, where "response" returns the predicted probabilities of the outcome variable.
Comments
Post a Comment