iris_classify_gradboost

iris_classify_gradboost, a scikit-learn code which uses the gradient boosting algorithm to classify the iris dataset and then estimates the probability uncertainties.

Licensing:

The computer code and data files described and made available on this web page are distributed under the MIT license

Related Data and Programs:

cancer_classify_gradboost, a scikit-learn code which uses the gradient boosting algorithm to classify the breast cancer dataset.

circle_classify_gradboost, a scikit-learn code which uses the gradient boost algorithm to classify the artificial circle dataset, and then determines the prediction uncertainties.

iris_classify_knn, a scikit-learn code which uses the k-nearest neighbor algorithm to classify the species of iris specimens based on sepal and petal measurements.

Reference:

• Andreas Mueller, Sarah Guido,
  Introduction to Machine Learning with Python,
  OReilly, 2017,
  ISBN: 978-1-449-36941-5

Source Code:

• iris_classify_gradboost.py, the source code.
• iris_classify_gradboost.sh, runs the script.
• iris_classify_gradboost.txt, the output file.