From the course: Applied Machine Learning: Foundations
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Decision trees
From the course: Applied Machine Learning: Foundations
Decision trees
Okay. Let's look at another very popular model, which is a decision tree. A decision tree is a model that looks at each feature independently and determines what is the best value to split that feature to give us the best predictions. One of the cool things about a decision tree is that because it's not throwing data into n-dimensional space, we actually don't need to standardize our data to use a decision tree. And also because decision trees can look at features independently, some decision trees can even deal with categorical features or deal with missing data. That makes them convenient, in that, you can get away with less pre-processing of your data to use a decision tree. However, in our case, we've already done that pre-processing, so we don't need to worry about that. And it turns out that, for example, standardizing the data before throwing it into a decision tree is not going to change the results of that. And the rest of this looks pretty much the same as what we saw…
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