J6.1 Feature Engineering on Rainfall Matrices

Monday, 11 January 2016: 4:00 PM
Room 354 ( New Orleans Ernest N. Morial Convention Center)
Mike Kim, Booz Allen Hamilton, VA

We apply traditional machine learning techniques to the problem of predicting expected rainfall. In terms of feature engineering where each observation is a real valued matrix, we apply the TF-IDF to SVD transformation popular in natural language processing. Given each observation is a matrix, we consider Pearson correlation between the columns of each observation matrix. Furthermore, we construct meta features based upon a gradient boosting model trained on the training data set's outliers. When these methods are used in conjunction with a bagged gradient boosting model, we scored 11th place on Kaggle's How Much Did It Rain? II challenge.
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