Skip to main content

Implement machine learning algorithms with python without sklearn.

Project description


Implement machine learning algorithms with python without sklearn. Some classes are design especially for CQU-ML course by Professor.He


  • With pip : pip3 install cquai-ml
  • With src : Clone or fork this project, then build it with python3 install


In most cases, API in this project is similar to scikit-learn project.

For example, if you want to run a decision tree classifier based on C4.5 (While scikit-learn use opt-CART instead of C4.5)

from cquai_ml import DecisionTreeClassifier
from sklearn.datasets import load_breast_cancer # get a dataset

X, y = load_breast_cancer(return_X_y=True)

clf1 = DecisionTreeClassifier(max_depth=1).fit(X, y)
pred1 = clf1.predict(X)


Everyone is welcomed to contribute!

We currently provides:
  • DatasetSpace
  • UnionHypothesisSpace
  • LinearRegression
  • LogisticRegression
  • LinearDiscriminantAnalysis
  • KNeighborsClassifier
  • DecisionTreeRegressor
  • DecisionTreeClassifier

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for cquai-ml, version 1.0.7
Filename, size File type Python version Upload date Hashes
Filename, size cquai-ml-1.0.7.tar.gz (10.5 kB) File type Source Python version None Upload date Hashes View

Supported by

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page