Classifying Iris Plant Species
The famous iris dataset is a traditional statistics demonstration dataset for building classification models. It contains data for 3 different classes (species) of iris (Setosa, Versicolour, and Virginica) with 50 observations of each class. Each observation records the flower's petal length and width and the sepal length and width, together with the known class. The goal is to build a classification model to classify new observations of flowers.
This MLHub pre-built model package uses the R language to build a classification (decision) tree model to represent the knowledge discovered using a so-called recursive partitioning algorithm. The knowledge representation language (decision tree) is recognised as an easily understandable language.
To install and run the pre-built model:
$ pip install mlhub $ ml install iris $ ml configure iris $ ml demo iris