Machine Learning in Java - Second Edition

Dataset

For the challenge, Orange released a large dataset of customer data, containing about one million customers, described in ten tables with hundreds of fields. In the first step, they resampled the data to select a less unbalanced subset, containing 100,000 customers. In the second step, they used an automatic feature construction tool that generated 20,000 features describing the customers, which was then narrowed down to 15,000 features. In the third step, the dataset was anonymized by randomizing the order of features, discarding the attribute names, replacing the nominal variables with randomly generated strings, and multiplying the continuous attributes by a random factor. Finally, all of the instances were split randomly into ...