943892eb70874ee0725fc6870ac78253_file-e125b600c79a5e26e005a32706651d8f31ffe051.ipynb

data-mining-decision-trees.ipynb

Decision tree (categorical)
Let's try to construct a decision tree for the weather data. If we stick to scikit, unfortunately, we already hit a big limitation of its decision tree implementation:

scikit decision trees are only for numeric data!!

Our weather data, however, is categorical, so that we now need to do the attribute encoding that was discussed earlier.
We cannot simply replace strings as values (i.e., "Sunny" = 1, "Rainy" = 2, etc.), since scikit actually treats these values as numbers, but our data has no ordering. If our data would be ordinal, we could do this, since that would make sense (i.e., "Worst" = -2, "Neutral" = 0, "Best" = 2, for example).
So, we have to encoding our values in a one-hot encoding. For this, we can use a variety of approaches. Let's do pandas for now.

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