In this section, we are going to develop different types of classical ML models and evaluate their performances. We have already discussed in detail the development of models and their evaluation in Chapter 9, Hypothesis Testing and Regression and Chapter 10, Model Development and Evaluation. Here, we will dive directly into implementation.
We are going to use different types of following algorithms and evaluate their performances:
- Logistic regression
- Support vector machine
- K-nearest neighbor classifier
- Random forest classifier
- Decision tree classifier
- Gradient boosting classifier
- Gaussian Naive Bayes classifier
While going over each classifier in depth is out of the scope of this chapter and book, our aim here is to present how we can continue developing ML algorithms after performing EDA operations on certain databases:
- Let's first...
