No doubt, clustering may not work on some datasets. But the good news is that there are alternatives, such as a decision tree. This approach generally works better with nonnumerical data.
The start of a decision tree is the root node, which is at the top of the flow chart. From this point, there will be a tree of decision paths, which are called splits. At these points, you will use an algorithm to make a decision, and there will be a probability computed. At the end of the tree will be the leaf (or the outcome).
A famous example—in machine learning circles —is to use a decision tree for the tragic sinking of the Titanic. The model predicts the survival of a passenger based on three features: sex, age, and the number of spouses or children along (sibsp). Here’s how it looks, in Figure 3-5.
There are clear advantages for decision trees. They are easy to understand, work well with large datasets, and provide transparency with the model.
However, decision trees also have drawbacks. One is error propagation. If one of the splits turns out to be wrong, then this error can cascade throughout the rest of the model!
Next, as the decision trees grow, there will be more complexity as there will be a large number of algorithms. This could ultimately result in lower performance for the model .
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