A primary goal of predictive modeling is to find a reliable and effective predictive relationship between an available set of features and an outcome. Ineffective feature representations and the inclusion of irrelevant features are two key data characteristics that can prevent a model from demonstrating good performance. Feature Engineering and Selection: A Practical Approach for Predictive Models provides an extensive set of techniques for uncovering effective representations of the features for modeling the outcome and for finding an optimal subset of features to improve a model's predictive performance.