Learning Data Mining with Python - Second Edition

Using decision trees

We can import the DecisionTreeClassifier class and create a Decision Tree using scikit-learn:

from sklearn.tree import DecisionTreeClassifierclf = DecisionTreeClassifier(random_state=14)

We used 14 for our random_state again and will do so for most of the book. Using the same random seed allows for replication of experiments. However, with your experiments, you should mix up the random state to ensure that the algorithm's performance is not tied to the specific value.

We now need to extract the dataset from our pandas data frame in order to use it with our scikit-learn classifier. We do this by specifying the columns we wish to use and using the values parameter of a view of the data frame. The following code creates ...