Model success can be predicated by feature engineering, feature selection and class architecture. Feature engineering involves wrangling of data to enhance predictive power. Feature selection allows the machine to identify the features that are most important in the model; which allows the model to generalize to unseen data. Optimizing class architecture can improve the misclassification rate.
In this talk, the following concepts will be reviewed: