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Predicting Sports Winners with Decision Trees
Engineering new features
In the previous few examples, we saw that changing the features can have quite a
large impact on the performance of the algorithm. Through our small amount of
testing, we had more than 10 percent variance just from the features.
You can create features that come from a simple function in pandas by doing
something like this:
dataset["New Feature"] = feature_creator()
The feature_creator function must return a list of the feature's value for each
sample in the dataset. A common pattern is to use the dataset as a parameter:
dataset["New Feature"] = feature_creator(dataset)
You can create those features more directly by setting all the values to a single
"default" value, like 0 in the next line:
dataset["My New Feature"] = 0
You can then iterate over the dataset, computing the features as you go. We used
this format in this chapter to create many of our features:
for index, row in dataset.iterrows():
home_team = row["Home Team"]
visitor_team = row["Visitor Team"]
# Some calculation here to alter row
dataset.ix[index] = row
Keep in mind that this pattern isn't very efficient. If you are going to do this, try all of
your features at once. A common "best practice" is to touch every sample as little as
possible, preferably only once.
Some example features that you could try and implement are as follows:
• How many days has it been since each team's previous match? Teams may be
tired if they play too many games in a short time frame.
• How many games of the last five did each team win? This will give a more
stable form of the HomeLastWin and VisitorLastWin features we extracted
earlier (and can be extracted in a very similar way).
• Do teams have a good record when visiting certain other teams? For instance,
one team may play well in a particular stadium, even if they are the visitors.
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