codegauravg / MLPB

Machine Learning Problem Bible

Machine Learning Problem Bible (MLPB)

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 ML problems with sparse data? Got it. Want to compare Scikit-learn’s RandomForestRegressor with R’s randomForest? No problem. Need an example of predicting a ranked target variable? You’ve come to the right place.

How It Works

MLPB contains a directory of Projects. Within each project is a designated Data directory and one or more machine learning problem directories. This looks something like

Projects/

  Iris/
    Data/
      iris.csv
    PredictSpecies/
      predict_species_rf.R
      predict_species_rf.py
      predict_species_xgb.R
    PredictSepalLength/
      iris_sepal_length_xgb.R
      
  JobTitles/
    Data/
      jobtitles.csv
    PredictJobCategory/
      naive_bayes_model.py
      naive_bayes_model.R

Most of these directories should include a README.md file providing details about the data and/or problem. A single dataset can have have multiple problems, and a single problem can have multiple example models/solutions.

You can browse all the datasets and problems in MLPB's wiki. There, you can also search for problems with specific tags like [mult-class classification], [sparse-data], [NLP], etc.