In this chapter, we have gained a high-level view of various basic building blocks and subcomponents involved in statistical modeling and machine learning, such as mean, variance, interquartile range, p-value, bias versus variance trade-off, AIC, Gini, the area under the curve, and so on with respect to the statistics context. 

In the next chapter, we will be covering complete tree-based models such as decision trees, random forest, boosted trees, ensemble of models, and so on to improve accuracy!