Learning Data Mining with Python

Chapter 3
Now that we have our dataset, we can compute a baseline. A baseline is an accuracy
that indicates an easy way to get a good accuracy. Any data mining solution should
beat this.
In each match, we have two teams: a home team and a visitor team. An obvious
baseline, called the chance rate, is 50 percent. Choosing randomly will (over time)
result in an accuracy of 50 percent.
Extracting new features
We can now extract our features from this dataset by combining and comparing
the existing data. First up, we need to specify our class value, which will give
our classification algorithm something to compare against to see if its prediction
is correct or not. This could be encoded in a number of ways; however, for this
application, we will specify our class as 1 if the home team wins and 0 if the visitor
team wins. In basketball, the team with the most points wins. So, while the data set
doesn't specify who wins, we can compute it easily.
We can specify the data set by the following:
dataset["HomeWin"] = dataset["VisitorPts"] < dataset["HomePts"]
We then copy those values into a NumPy array to use later for our scikit-learn
classifiers. There is not currently a clean integration between pandas and scikit-learn,
but they work nicely together through the use of NumPy arrays. While we will use
pandas to extract features, we will need to extract the values to use them with
scikit-learn:
y_true = dataset["HomeWin"].values
The preceding array now holds our class values in a format that scikit-learn can read.
We can also start creating some features to use in our data mining. While sometimes
we just throw the raw data into our classifier, we often need to derive continuous
numerical or categorical features.
The first two features we want to create to help us predict which team will win
are whether either of those two teams won their last game. This would roughly
approximate which team is playing well.
We will compute this feature by iterating through the rows in order and recording
which team won. When we get to a new row, we look up whether the team won the
last time we saw them.
[ 45 ]