Draw Classification
Objective: Draw data and run existing code as an introduction to supervised learning methods.
The IPython Notebook lets a user draw data as a first step. The data is imported, summarized, visualized, transformed, scaled, and split in preparation for training.
This introductory exercise goes through all the steps to prepare the data, train the classifiers, and report the accuracy results. All of this is done with the data drawn at the beginning of the Notebook. Each result is different based on the overlap and shape of the data.
An activity at the end adds an additional classifier (AdaBoost) as a machine learning algorithm for classification of the drawn data.
ab = AdaBoostClassifier()
ab.fit(XA,yA)
yP = ab.predict(XB)
The activity provides an opportunity to train a new classifier using the examples above.
Classification with Google Colab
The activity is also available in Google Colab except that the DrawData package cannot access the Clipboard. A sample data set is provided to train the classifiers and generate confusion matrices.
Return to Classification Overview
