msitrees 0.1

MSI based machine learning algorithms

msitrees

msitrees is a set of machine learning models based on minimum surfeit and inaccuracy decision tree algorithm. So whats cool about them? No hyperparameters to optimize for base learner. Tree is regularized internally to avoid overfitting by design. Quoting authors of the paper:

To achieve this, the algorithm must automatically understand when growing the decision tree adds needless complexity, and must measure such complexity in a way that is commensurate to some prediction quality aspect, e.g., inaccuracy. We argue that a natural way to achieve the above objectives is to define both the inaccuracy and the complexity using the concept of Kolmogorov complexity.

For convenience, msitrees comes with scikit-learn style API and can be used with sklearn functions accepting estimator object as parameter.

Installation

With pip

pip install msitrees

From source

git clone https://github.com/xadrianzetx/msitrees.git
cd msitrees
python setup.py install

Windows builds require at least MSVC2015

Quick start

from msitrees.tree import MSIDecisionTreeClassifier
from sklearn.datasets import load_iris
from sklearn.model_selection import cross_val_score

data = load_iris()
clf = MSIDecisionTreeClassifier()
cross_val_score(clf, data['data'], data['target'], cv=10)

# array([1.        , 1.        , 1.        , 0.93333333, 0.93333333,
    #    0.8       , 0.93333333, 0.86666667, 0.8       , 1.        ])

Reference documentation

API documentation is available on here.