Learning Data Mining with Python

Chapter 3
Using decision trees
We can import the DecisionTreeClassifier class and create a decision tree using
scikit-learn:
from sklearn.tree import DecisionTreeClassifier
clf = 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 a dataset using our last win values for both the home
team and the visitor team:
X_previouswins = dataset[["HomeLastWin", "VisitorLastWin"]].values
Decision trees are estimators, as introduced in Chapter 2, Classifying with scikit-learn
Estimators, and therefore have fit and predict methods. We can also use the
cross_val_score method to get the average score (as we did previously):
scores = cross_val_score(clf, X_previouswins, y_true,
scoring='accuracy')
print("Accuracy: {0:.1f}%".format(np.mean(scores) * 100))
This scores 56.1 percent: we are better than choosing randomly! We should be
able to do better. Feature engineering is one of the most difficult tasks in data
mining, and choosing good features is key to getting good outcomes—more so
than choosing the right algorithm!
Sports outcome prediction
We may be able to do better by trying other features. We have a method for
testing how accurate our models are. The cross_val_score method allows us
to try new features.
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