Chapter 6

Exploring Four Simple and Effective Algorithms

IN THIS CHAPTER

check Using linear and logistic regression

check Understanding Bayes theorem and using it for naïve classification

check Predicting on the basis of cases being similar with kNN

“The goal is to turn data into information, and information into insight.”

— CARLY FIORINA

In this chapter, you start to explore all the algorithms and tools necessary for learning from data (the training phase) and being capable of predicting a numeric estimate (for example, house pricing) or a class (for instance, the species of an Iris flower) given a new example that you didn’t have before. In this chapter, you start with the simplest algorithms and work toward more complex ones.

remember Simple and complex aren’t absolute values in machine learning — they’re relative to the algorithm’s construction. Some algorithms are simple summations while others require complex calculations (and Python deals with both the simple and complex algorithms for you). It’s the data that makes the difference: For some problems, simple algorithms are better; other problems may ...

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