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MALSS: MAchine Learning Support System

Project description

malss is a python module to facilitate machine learning tasks. This module is written to be compatible with the scikit-learn algorithms and the other scikit-learn-compatible algorithms.


These are external packages which you will need to install before installing malss.

  • python (>= 3.6)
  • numpy (>= 1.10.2)
  • scipy (>= 0.16.1)
  • scikit-learn (>= 0.19)
  • matplotlib (>= 1.5.1)
  • pandas (>= 0.14.1)
  • jinja2 (>= 2.8)

I highly recommend Anaconda. Anaconda conveniently installs packages listed above.


If you already have a working installation of numpy and scipy:

pip install malss

If you have not installed numpy or scipy yet, you can also install these using pip.



from malss import MALSS
from sklearn.datasets import load_iris
iris = load_iris()
clf = MALSS('classification'),, 'classification_result')


from malss import MALSS
from sklearn.datasets import load_boston
boston = load_boston()
clf = MALSS('regression'),, 'regression_result')

Change algorithm:

from malss import MALSS
from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier as RF
iris = load_iris()
clf = MALSS('classification'),, algorithm_selection_only=True)
algorithms = clf.get_algorithms()
# check algorithms here
                  [{'n_estimators': [10, 30, 50],
                    'max_depth': [3, 5, None],
                    'max_features': [0.3, 0.6, 'auto']}],
                  'Random Forest'),, 'classification_result')


View the documentation here.

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