MLPB is meant to become an organized collection of machine learning problems and solutions. In practice, machine learning often goes like this
I have this problem... I need to classify something as A, B or C using a combination of numeric and categorical features. If I could find a similar problem, maybe I could modify the solution to work for my needs.
This is where MLPB steps in. Want to see machine learning problems with sparse data? Got it. Want to compare Scikit-learn’s RandomForestRegressor with R’s randomForest? Got it. Need an example of predicting a ranked target variable? Got it.
MLPB contains a directory of Problems. Within each problem is a designated _Data directory and one or more scripts with a solution to the problem. This looks something like
Problems/
Classify Iris Species/
_Data/
iris.csv
train.csv
test.csv
predict_species_xgb.R
Predict NFL Game Winner/
_Data/
train.csv
test.csv
random_forest_model.py
random_forest_model.R
Most of these directories should include a README.md file providing details about the problem, data, and solution(s). You can browse all the problems in MLPB's wiki. You can also search for problems with specific tags like [mult-class classification], [sparse-data], [NLP], etc.
If you'd like to contact me regarding bugs, questions, or general consulting, feel free to drop me a line - [email protected]
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