Summary
Supervised learning is the predominant technique used in machine learning applications. The methodology consists of a series of steps beginning with data exploration, data transformation, and data sampling, through feature reduction, model building, and ultimately, model assessment and comparison. Each step of the process involves some decision making which must answer key questions: How should we impute missing values? What data sampling strategy should we use? What is the most appropriate algorithm given the amount of noise in the dataset and the prescribed goal of interpretability? This chapter demonstrated the application of these processes and techniques to a real-world problem—the classification problem using the UCI Horse Colic dataset.
Whether the problem is one of classification, when the target is a categorical value, or Regression, when it is a real-valued continuous variable, the methodology used for supervised learning is similar. In this chapter, we have used classification...
