By Acodemy
ISBN-10: 151865987X
ISBN-13: 9781518659874
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Extra resources for Learn Web Scraping With Python In A Day: The Ultimate Crash Course to Learning the Basics of Web Scraping With Python In No Time
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At first, we will write classification code ourselves in order to understand the concepts, but we will quickly switch to using scikit-learn whenever possible. The goal is to first understand the basic principles of classification and then progress to using a state-of-the-art implementation. [ 29 ] Classifying with Real-world Examples The Iris dataset The Iris dataset is a classic dataset from the 1930s; it is one of the first modern examples of statistical classification. The dataset is a collection of morphological measurements of several Iris flowers.
The goals of a good feature are to simultaneously vary with what matters (the desired output) and be invariant with what does not. For example, compactness does not vary with size, but varies with the shape. In practice, it might be hard to achieve both objectives perfectly, but we want to approximate this ideal. You will need to use background knowledge to design good features. Fortunately, for many problem domains, there is already a vast literature of possible features and feature-types that you can build upon.
The search procedure: How do we find the model we need to use? In our case, we tried every possible combination of feature and threshold. You can easily imagine that as models get more complex and datasets get larger, it rapidly becomes impossible to attempt all combinations and we are forced to use approximate solutions. In other cases, we need to use advanced optimization methods to find a good solution (fortunately, scikit-learn already implements these for you, so using them is easy even if the code behind them is very advanced).