Predictions Don’t Grow on Trees, or Do They?
Our goal in this chapter is to see and apply concepts learned from previous chapters in order to construct and use modern learning algorithms to glean insights and make predictions on real datasets. While we explore the following algorithms, we should always remember that we are constantly keeping our metrics in mind.
In this chapter, we will be looking at the following ML algorithms:
- Performing naïve Bayes classification
- Understanding decision trees
- Diving deep into unsupervised learning (UL)
- k-means clustering
- Feature extraction and principal component analysis (PCA)
