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Machine Learning in Python

pipelines for faster workflow and cleaner presentation

1. process_data: pipeline for processing data

  • inputs: list of categorical and continuous variables, and list of variables to be left alone; X
  • output: a dataframe X with categorical variables one-hot encoded with drop_first, and continous variables standardized

2. good_fit: pipeline for hyper parameter tuning

  • inputs: objective: {'clf', 'reg'}, base model, X_train, X_test, y_train, y_test, random_state, cross validation folds
  • outputs:
    1. a log of parameters with which to tune the models, as well as training and testing error metrics, all cross-validated with k folds as specified by the user; returned after calling function and stored as attribute
    2. a list of fitted estimators in accordance to the sequence of combinations of parameters; stored as attribute

the important thing here is that the results are returned as a log as opposed to a single outcome as in sklearn, and that the fitted estimators are not lost but stored, for use through indexing anytime

3. compare: class and methods for comparing forests/trees, for Tencent

  • inputs: 2 fitted models of the same kind:{'RandomForestClassifier', 'ExtraTreeClassifier', 'DecisionTreeClassifier'}
  • outputs: a table of feature importances changes and related statistics, and a smaller table of features with the most drastic increase/decrease in feature importances and related statistics

this is for comparison of two models with the same X but different ys, e.g. in marketing analytics where the X is user profile and activity data, but the ys being different metrics e.g. 2nd day retention binary outcome and 14th day retention binary outcome

more updates to follow