After several examples, it is now time to predict ad click-through with the decision tree algorithm we have just thoroughly learned about and practiced. We will use the dataset from a Kaggle machine learning competition, Click-Through Rate Prediction (https://www.kaggle.com/c/avazu-ctr-prediction). The dataset can be downloaded from https://www.kaggle.com/c/avazu-ctr-prediction/data.
Only the train.gz file contains labeled samples, so we only need to download this and unzip it (it will take a while). In this chapter, we focus on only the first 300,000 samples from the train file unzipped from train.gz.
The fields in the raw file are as follows:
We take a glance at the head of the file by running the following command:
head train | sed 's/,,/, ,/g;s/,,/, ,/g' | column -s, -t
Rather than simple head train, the output is...
