This is the well known Iris dataset from Fisher's classic paper (Fisher, 1936). The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant. One class is linearly separable from the other 2; the latter are NOT linearly separable from each other.
You have just been hired by "Super-Flora", a flower distribution chain, as "Data Scientist". The Flower Purchasing Manager suspects one of his suppliers of dishonesty. Versicolor Irises usually last longer and are more expensive than others. But they look a lot like Iris Setosa, and the supplier may have sold you a certain number of Setosa instead to make better profit margins. Your task is to check the batches that arrive. The botanist has indicated that those flowers have features, such as the dimensions of the petals, sepals, stem length, color, etc. which allows us to distinguish them. The Flower Purchasing Manager hired the botanist to measure some features of a few flowers in each lot, to create a small dataset for a pilot study.
The Flower Purchasing Manager gives you access to a training set with "truth values" of the identity of the flowers. You must provide a "classifier", which is a program capable of predicting the identity of the flowers given the measured features, for new test examples.
References and credits:
R. A. Fisher. The use of multiple measurements in taxonomic problems. Annual Eugenics, 7, Part II, 179-188 (1936).
The competition protocol was designed by Isabelle Guyon.
The starting kit was adapted from an Jupyper notebook designed by Balazs Kegl for the RAMP platform.
This challenge was generated using Chalab, a competition wizard designed by Laurent Senta.
The problem is a multiclass classification problem. Each sample (an Iris) is characterized by its sepal and petal width and length (4 features). You must predict the Iris categories: setosa, virginica, or versicolor.
You are given for training a data matrix X_train of dimension num_training_samples x num_features and an array y_train of labels of dimension num_training_samples. You must train a model which predicts the labels for two test matrices X_valid and X_test.
There are 2 phases:
This sample competition allows you to submit either:
The submissions are evaluated using the accuracy metric.
Submissions must be made before the end of phase 1. You may submit 5 submissions every day and 100 in total.
This challenge is governed by the general ChaLearn contest rules.
Start: Nov. 15, 2018, midnight
Description: Development phase: tune your models and submit prediction results, trained model, or untrained model.
Start: April 30, 2019, midnight
Description: Final phase (no submission, your last submission from the previous phase is automatically forwarded).
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