Iris

Please log in.

    • Iris

      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.

    • Iris: Evaluation

      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.

    • Iris: Rules

      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.

    • Iris: Data

      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

    • Development

      starting_kit public_data
  • Make your submission using github

    ID Status Inputs