Logistic regression is a form of probabilistic statistical classification model, which can be used to predict class labels based on one or more features. The classification is done by using the logit
function to estimate the outcome probability. One can use logistic regression by specifying the family as a binomial while using the glm
function. In this recipe, we will introduce how to classify data using logistic regression.
You need to have completed the first recipe by generating training and testing datasets.
Perform the following steps to classify the churn data with logistic regression:
- With the specification of family as a binomial, we apply the
glm
function on the datasettrainset
, by using churn as a class label and the rest of the variables as input features:
> fit = glm(churn ~ ., data = trainset, family=binomial)
- Use the
summary
function to obtain summary information of the built logistic regression model...