train a logistic regression model in r

Training a logistic regression model in R can be accomplished using the glm() function, which stands for generalized linear model. Here's an example of how to train a logistic regression model using the built-in mtcars dataset in R:

# load the mtcars dataset
data(mtcars)

# fit the logistic regression model using glm()
model <- glm(vs ~ mpg + hp, data = mtcars, family = binomial())

# print the model summary
summary(model)
194 chars
9 lines

In the above example, we're predicting the vs variable (engine cylinder configuration: V-shaped (0) or straight (1)) using the mpg (miles per gallon) and hp (horsepower) variables. We pass these input variables to the formula argument of the glm() function (vs ~ mpg + hp). The family argument specifies the type of model we're fitting (in this case, binary logistic regression).

Once the model is trained, we use the summary() function to print out the model summary, which includes information about the model coefficients, significance levels, and goodness-of-fit measures such as the deviance and AIC.