Evaluation of machine learning algorithms and feature engineering procedures

It is important to note that in literature, oftentimes there is a stark contrast between the terms features and attributes. The term attribute is generally given to columns in tabular data, while the term feature is generally given only to attributes that contribute to the success of machine learning algorithms. That is to say, some attributes can be unhelpful or even hurtful to our machine learning systems. For example, when predicting how long a used car will last before requiring servicing, the color of the car will probably not very indicative of this value.

In this book, we will generally refer to all columns as features until they are proven to be unhelpful or hurtful. When this happens, we will usually cast those attributes aside in the code. It is extremely important, then, to consider the basis for this decision. How does one evaluate a machine learning system and then use this evaluation to perform feature engineering?