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Python for Data Science For Dummies by Luca Massaron, John Paul Mueller

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Chapter 17

Exploring Four Simple and Effective Algorithms

In This Chapter

arrow Using linear and logistic regression

arrow Understanding Bayes theorem and using it for naive classification

arrow Predicting on the basis of cases being similar with kNN

In this new part, 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 instead require complex algorithms.

You don’t have to type the source code for this chapter ...

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