Brought to you by ChaLearn
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.
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.
This challenge was generated using ChaLab.
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.
To prepare your submission, remember to use predict_proba, which provides a matrix of prediction scores scaled between 0 and 1. The dimension of the matrix is num_pattern x num_classes. Each line represents the probabilities of class membership, which sum up to one. Preparing your submission with the starting kit is the easiest.
There are 2 phases:
- Phase 1: development phase. We provide you with labeled training data and unlabeled validation and test data. Make predictions for both datasets. However, you will receive feed-back on your performance on the validation set only. The performance of your LAST submission will be displayed on the leaderboard.
- Phase 2: final phase. You do not need to do anything. Your last submission of phase 1 will be automatically forwarded. Your performance on the test set will appear on the leaderboard when the organizers finish checking the submissions.
This sample competition allows you to submit either:
- Only prediction results (no code).
- A pre-trained prediction model.
- A prediction model that must be trained and tested.
The submissions are evaluated using the mse_metric metric. This metric computes the balanced accuracy (that is the average of the per class accuracies). The metric is re-scaled linearly between 0 and 1, 0 corresponding to a random guess and 1 to perfect predictions.
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.
This challenge relies on the iris dataset from UCI repository. See the full description here.
You can download the data and a starting kit allowing you to generate a baseline submission from the "Files" tab.
To get started it's recommended to fork this repository and follow this instruction to integrate your repo into codalab