Multi-target Random Forest implementation that can mix both classification and regression tasks.

Morfist implements the Random Forest algorithm (Breiman, 2001) with support for mixed-task multi-task learning, i.e., it is possible to train the model on any number of classification tasks and regression tasks, simultaneously. Morfist's mixed multi-task learning implementation follows that proposed by Linusson (2013).

• Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.
• Linusson, H. (2013). Multi-output random forests.

With pip:

pip install decision-tree-morfist

With conda:

conda install -c systemallica decision-tree-morfist

• Similarly to a scikit-learn RandomForestClassifier, a MixedRandomForest can be initialised in this way:

from morfist import MixedRandomForest

mrf = MixedRandomForest(
    n_estimators=n_trees,
    min_samples_leaf=1,
    classification_targets=[0]
)

For more info on the possible parameters, visit the documentation.

• Once the model is initialised, it can be fitted like this:

  mrf.fit(X, y)
  

  Where X are the training examples and Y are their respective labels(if they are categorical) or values(if they are numerical)

• The model can be now used to predict new instances.
  • Class/value:

  mrf.predict(x)
  

  • Probability:

  mrf.predict_proba(x)
  

• Speed up the learning algorithm implementation (morfist is currently much slower than the Random Forest implementation available in scikit-learn)