The objective of the competition is to help us build as good a model as possible so that we can, as optimally as this data allows, relate molecular information, to an actual biological response.
\r\nWe have shared the data in the comma separated values (CSV) format. Each row in this data set represents a molecule. The first column contains experimental data describing an actual biological response; the molecule was seen to elicit this response (1),\r\n or not (0). The remaining columns represent molecular descriptors (d1 through d1776), these are calculated properties that can capture some of the characteristics of the molecule - for example size, shape, or elemental constitution. The descriptor matrix has\r\n been normalized.
", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Rules", "content": "\r\n\t\r\nYou 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\n\r\n Team mergers are allowed and can be performed by the team leader. In order to merge, the combined team must have a total submission count less than or equal to the maximum allowed as of the merge date. The maximum allowed is the number of submissions per day multiplied by the number of days the competition has been running.
\r\n
There is no maximum team size.
\r\nYou may submit a maximum of 2 entries per day.
\r\nYou may select up to 5 final submissions for judging.
\r\n\r\n Because the data can be manually decoded and thus an entry can be created by hand, scoring data will be withheld until you have locked down a model.\r\n
\r\n\r\n You must upload all kernels and model files alongside documentation in a single zip file\r\n in order for your entry to be eligible. You can do this at the time of submission, or append\r\n it to your submission at a later time (before the competition end).\r\n
\r\nStart Date: 3/16/2012 7:10 PM UTC
\r\nMerger Deadline: None
\r\nEntry Deadline: None
\r\nEnd Date: 6/15/2012 11:59 PM UTC
\r\n\t\r\nPredicted probabilities that a molecule elicits a response are evaluated using the log loss metric.
\r\nLog loss is defined as:
\r\n$$\\text{log loss}=-\\frac{1}{N}\\sum_{i=1}^Ny_i\\log\\left(\\hat{y_i}\\right)+\\left(1-y_i\\right)\\log\\left(1-\\hat{y_i}\\right),$$
\r\nwhere \\\\(N\\\\) is the number of samples, \r\n\\\\(\\text{log}\\\\) is the natural logarithm, \\\\(\\hat{y_i}\\\\) is the posterior probability that the\r\n\\\\(i^{th}\\\\) sample elicited a response, and\r\n\\\\(y_i\\\\) is the ground truth \r\n(\\\\(y_i=1\\\\) means the molecule elicited a response, \r\n\\\\(y_i=0\\\\) means that it did not).
", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Prizes", "content": "The data is in the comma separated values (CSV) format. Each row in this data set represents a molecule. The first column contains experimental data describing a real biological response; the molecule was seen to elicit this response (1), or not (0). The\r\n remaining columns represent molecular descriptors (d1 through d1776), these are caclulated properties that can capture some of the characteristics of the molecule - for example size, shape, or elemental constitution. The descriptor matrix has been normalized.
", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false } ] }