You cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts.
\r\n\r\n Privately sharing code or data outside of teams is not permitted.\r\n It's okay to share code if made available to all participants on the forums.\r\n
\r\nTeam mergers are not allowed in this competition.
\r\n\r\nThere is no maximum team size.
\r\nYou may submit a maximum of 2 entries per day.
\r\nYou may select up to 2 final submissions for judging.
\r\nStart Date: TBA
\r\nMerger Deadline: None
\r\nEntry Deadline: None
\r\nEnd Date: 2/1/2014 12:00 AM UTC
\r\n\t\r\nThe rules page is a bit special: it will auto-update the text based on the parameters of the competition. The rules you need to add here are any additional rules relating to dataset usage. You may also put grading information here.
", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "data-description", "content": "There are two aspects of the modeling that will be evaluated. One aspect is the predictive ability of the model and the other is the inferences about the causal effect of manganese on cognitive ability.
\n\nTo evaluate predictive ability, we will assess the root mean square error of the predictions on the test set, which is defined by:
\n\\[ RMSE = \\sqrt{\\frac{1}{n}\\sum_{i=1}^{n}(Y_i - \\widehat{Y}_i)^2} \\]
\nFor this aspect of the modeling you will be allowed to upload predictions on Kaggle and the performance on approximately a random half of the data is given so that you can gauge your performance on out-of-sample data. The final evaluation will be done on the remaining half so that you do not overfit the test data.
\nTo submit predictions on Kaggle, you should have a CSV file where the first column is the observation ID (ID) and the second column is the corresponding prediction (Y) as you see below.\n The file should include predictions for all 9,588 observations (with matching observation IDs) in the testing set.
\nID,Y\n\n
1,208.3
2,204.5
3,249.2
etc.
The causal inference aspect will be evaluated by the distance to the causal effect among the test set:
\n\\[ \\frac{1}{n_{test}}\\sum_{i=1}^{n_{test}} Y_{X_1=17,i}-Y_{X_1=4,i}. \\]
\nNote that the training set and test set are randomly drawn from the same underlying population. For this aspect of the evaluation you are asked to submit your model and code on canvas at the project deadline. The performance will then be evaluated based on the submission. Please, refer to the document submitted on canvas for additional questions and information.\n", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false } ] }