Identify features and labels in a dataset for machine learning
As you learned in Chapter 3, Identify Common Machine Learning Techniques, features and labels are two fundamental concepts that define the data you work with when training ML models.
Features are individual measurable properties or characteristics of whatever is being observed. In ML models, features are used as input variables. These are the data points that you use to make predictions. For example, if you’re trying to predict the price of a house, the features might include the number of bedrooms, the size of the house in square feet, the neighborhood it’s in, how close it is to a fire station, or what the local property tax rates are. Features are represented by independent variables in your dataset that you believe will help you make accurate predictions about your target variable.
Labels, on the other hand, are the output you’re trying to predict or classify.
In supervised learning (SL...
