Summary
In this chapter, we learned about the machine learning process. The various steps are iterative in nature and ensure that the data is processed in a systematic manner. We explored the various evaluation metrics for evaluating a trained model. We also covered two types of variables: categorical and numeric.
We covered the different ways to view the summary of the data. We also delved into the various plots available in R for visualizing the data and for performing EDA. We looked at the German Credit, Boston Housing, and Diabetes datasets and performed some visualization on these datasets to understand them better. We also learned how to plot correlations for the features in the data and the ways to interpret them.
We looked into the common machine learning models used by data scientists. We also came to understand some of the types of models that can be used for numeric prediction and categorical prediction. Furthermore, we implemented a classifier in R and interpreted the results. We explored built-in datasets for building linear regression and classifier models.
