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Examples that use Collective Learning

This is a list of examples that we've implemented to show you how to use Collective Learning

Mnist

Uses the standard Mnist database of handwritten images

  • mnist_keras. Uses the KerasLearner helper class. Discussed in more detail here.
  • mnist_pytorch. Uses the PytorchLearner helper class. Discussed in more detail here.

Fraud

The fraud dataset consists of information about credit card transactions. The task is to predict whether transactions are fraudulent or not. The data needs to be downloaded from Kaggle, and the data directory passed in with the flag --data_dir.

  • fraud_mli. Uses the MachineLearningInterface directly and detects fraud in bank transactions.
  • fraud_keras. Loads data from numpy arrays and uses KerasLearner.

Cifar10

Uses the standard Cifar10 database of images

Xray

A binary classification task that requires predicting pneumonia from images of chest X-rays. The data need to be downloaded from Kaggle, and the data directory passed in with the flag --data_dir

Iris

Uses the standard Iris dataset. The aim of this task is to classify examples into one of three iris species based on measurements of the flower.

  • iris_random_forest. Uses the MachineLearningInterface directly and a random forest for classification.