concept input feature in category machine learning

This is an excerpt from Manning's book Real-World Machine Learning. Login to get full access to this book.

In this case, you’d use the Marital status variable as the target, or label, and the remaining variables as features. The job of the ML algorithm will then be to find how the set of input features can successfully predict the target. Then, for people whose marital status is unknown, you can use the model to predict marital status based on the input variables for each individual. Figure 1.9 shows this process on our toy dataset.

Figure 1.9. The machine-learning modeling process

At this point, think of the ML algorithm as a magical box that performs the mapping from input features to output data. To build a useful model, you’d need more than two rows. One of the advantages of machine-learning algorithms, compared with other widely used methods, is the ability to handle many features. Figure 1.9 shows only four features, of which the Person ID and Name probably aren’t useful in predicting marital status. Some algorithms are relatively immune to uninformative features, whereas others may yield higher accuracy if you leave those features out. Chapter 3 presents a closer look at types of algorithms and their performance on various kinds of problems and datasets.

Figure 2.11. Four visualization techniques, arranged by the type of input feature and response variable to be plotted

The previous chapter covered guidelines and principles of data collection, preprocessing, and visualization. The next step in the machine-learning workflow is to use that data to begin exploring and uncovering the relationships that exist between the input features and the target. In machine learning, this process is done by building statistical models based on the data. This chapter covers the basics required to understand ML modeling and to start building your own models. In contrast to most machine-learning textbooks, we spend little time discussing the various approaches to ML modeling, instead focusing attention on the big-picture concepts. This will help you gain a broad understanding of machine-learning model building and quickly get up to speed on building your own models to solve real-world problems. For those seeking more information about specific ML modeling techniques, please see the appendix.

Let’s frame the discussion of ML modeling around an example. Recall the Auto MPG dataset from chapter 2. The dataset contains metrics about automobiles, such as manufacturer region, model year, vehicle weight, horsepower, and number of cylinders. The purpose of the dataset is to understand the relationship between the input features and a vehicle’s miles per gallon (MPG) rating.