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GSClassification Tool

A multi-parameter, multi-model k-fold grid search. Designed to find the most promissing model for your classification problem.

Modules

data_preprocessing.py

A template to import, treat and export data in the correct format. Highly dependent on your goals.

GSClassification.py

The classification module containing the classification class.

Current compatible classifiers

  • SVC
  • DecisionTreeClassifier
  • KNeighborsClassifier
  • LogisticRegression
  • GaussianNB
  • RandomForestClassifier
  • Perceptron
  • SGDClassifier
  • XGBClassifier

Run

model_evaluation.py

A script which joins data_preprocessing and GSClassification modules, in order to return statistics about each of the evaluated classifiers and their best parameters.

You need to set:

  • Classifiers (comment out the ones you do not wish to include)
  • Grid search parameters

Example

This software comes with an example from Kaggle's introductory competition. It will preprocess data, test different parameters for different models (executing k-fold grid searches to avoid overfitting) and return

  • a sorted pandas dataframe with the best accuracies and parameters;
  • the cumulative accuracy profile (CAP) along with an image representation of the above metrics.

In order to modify parameters of the grid search, check sklearn's GridSearchCV guide.