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. These measurements will enable us to distinguish multiple species of flower. Today, species are identified by their DNA fingerprints, but in the 1930s, DNA's role in genetics had not yet been discovered.

The following four attributes of each plant were measured:

• Sepal length
• Sepal width
• Petal length
• Petal width

In general, we call the individual numeric measurements we use to describe our data features. These features can be directly measured or computed from intermediate data.

This dataset has four features. Additionally, for each plant, the species is recorded. The problem we want to solve is: "given these examples, if we see a new flower out...

The model discussed in the previous section is a simple model; it achieves 96.0 percent accuracy of the whole data. However, this evaluation is almost certainly overly optimistic. We used the data to define what the tree would look like, and then we used the same data to evaluate the model. Of course, the model will perform well on this dataset as it was optimized to perform well on it. The reasoning is circular.

What we really want to do is estimate the ability of the model to generalize to new instances. We should measure its performance in instances that the algorithm has not seen at training. Therefore, we are going to do a more rigorous evaluation and use held-out data. To do this, we are going to break up the data into two groups: in one group, we'll train the model, and in the other, we'll test the one...

How do we decide which classifier is best? Rarely do we find the perfect solution, the model that never makes any mistakes, so we need to decide which one to use. We used accuracy before, but sometimes it will be better to optimize so that the model makes fewer errors of a specific kind. For example, in spam filtering, it may be worse to delete a good email than to erroneously let a bad email through. In that case, we may want to choose a model that is conservative in throwing out emails rather than the one that just makes the fewest mistakes overall. We can discuss these issues in terms of gain (which we want to maximize) or loss (which we want to minimize). They are equivalent, but sometimes one is more convenient than the other and you will read articles discussing minimizing the loss or maximizing the gain.

In a medical setting, false...

We will now look at a slightly more complex dataset. This will include the introduction of a new classification algorithm and a few other ideas.

We now look at another agricultural dataset, which is still small, but already too large to plot exhaustively on a page as we did with the Iris dataset. This dataset consists of measurements of wheat seeds. There are seven features that are present, which are as follows:

• Area A
• Perimeter P
• Compactness C = 4πA/P²
• Length of kernel
• Width of kernel
• Asymmetry coefficient
• Length of kernel groove

There are three classes corresponding to three wheat varieties: Canadian, Koma, and Rosa...

So far, we have looked at two classical classifiers, namely the decision tree and the nearest neighbor classifier. Scikit-learn supports many more, but it does not support everything that has ever been proposed in academic literature. Thus, one may be left wondering: which one should I use? Is it even important to learn about all of them?

In many cases, knowledge of your dataset may help you decide which classifier has a structure that best matches your problem. However, there is a very good study by Manuel Fernández-Delgado and his colleagues titled, Do we Need Hundreds of Classifiers to Solve Real World Classification Problems? This is a very readable, very practically-oriented study, where the authors conclude that there is actually one classifier which is very likely to be the best (or close to the best) for a majority of problems, namely random...

Classification means generalizing from examples to build a model that assigns objects to a predefined class (that is, a rule that can automatically be applied to new, unclassified objects). It is one of the fundamental tools in machine learning and we will look at many more examples of this in the forthcoming chapters.

In a way, this was a very abstract and theoretical chapter, as we introduced generic concepts with simple examples. We went over a few operations with the Iris dataset. This is a small dataset. However, it has the advantage that we were able to plot all the data and see what we were doing in detail. This is something that will be lost when we move on to problems with many dimensions and many thousands of examples. The insight we gained here will still be valid.

You also learned that the training error is a misleading, over-optimistic estimate of how well...