Lately, I’ve written a few iterations of pyspark to develop a recommender system (I’ve had some practice creating recommender systems in pyspark). I ran into a situation where I needed to generate some recommendations on some different datasets. My problem was that I had to decipher some of the prediction documentation. Because of my struggles, […]
Summary: Writing better quality data mining code requires you to write code that is self-explanatory and does one thing at a time well. In terms of analysis, you should be cross-validating and watching for slowly changing relationships in the data.
Summary: XGBoost and ensembles take the Kaggle cake but they’re mainly used for classification tasks. Some tools like factorization machines and vowpal wabbit make occasional appearances.
Summary: To stay on top of your personal development, try learning new things like a programming language, an instrument, or exposure to a new field (e.g. biology or accounting). Exposure to new ideas helps you avoid confirmation bias and increase you willingness to explore your analysis further.
Summary: The foreach package provides parallel operations for many packages (including randomForest). Packages like gbm and caret have parallelization built into their functions. Other tools like bigmemory and ff solve handling large datasets with memory management.