Building Classification Models

In many real-world scenarios, you are not only interested in predicting numeric values but also in making decisions or classifying observations into distinct groups. This is where classification problems come into play. A classification problem involves predicting a categorical outcome—such as whether an email is spam or not, or if a patient has a particular disease—based on a set of input features. The two most common types of classification models you will encounter are logistic regression and decision trees. Logistic regression is especially useful for binary classification tasks, while decision trees can handle both binary and multiclass problems, offering interpretable rules for decision making. Both of these models can be easily implemented in R using the Tidymodels suite, which provides a consistent interface for model specification, training, and evaluation.

To build a classification model in Tidymodels, you start by specifying the type of model you want to use, such as logistic_reg() for logistic regression. The model specification defines the algorithm and its settings, including the computational engine (like "glm" for generalized linear models) and the mode ("classification" or "regression"). Once specified, you fit the model to your training data using the fit() function, providing the formula and data. The output of a fitted logistic regression model includes estimated coefficients for each predictor, which represent the change in the log-odds of the outcome for a one-unit increase in the predictor, holding other variables constant. By examining the summary of the fitted model, you can interpret which features are most influential in predicting the class and the direction of their effects. This interpretability is one of the strengths of logistic regression in classification tasks.