You cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts.
\n\n Privately sharing code or data outside of teams is not permitted.\n It's okay to share code if made available to all participants on the forums.\n
\nTeam mergers are not allowed in this competition.
\n\nThe maximum size of a team is 1 participant.
\nYou may submit a maximum of 5 entries per day.
\nYou may select up to 2 final submissions for judging.
\n\t\n", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "data-description", "content": "File descriptions\n-----------------\n\n - training.csv - the training set\n - testing.csv - the test set\n - sample_valid_submission.csv - a sample submission file in the correct format\n - additional_training.csv - the additional training set with several missing feature values. NaN denotes missing feature value.\n - annotation_confidence.csv - additional information about the training data. The sunny or not sunny label for each data is based on a response from 3 human annotators. An annotation confidence of 1 is when all three annotators agree on the label, and 0.66 is when only two out of three annotators agree on the label.\n - test_proportions.csv - additional information about the test class proportions\n\nData fields\n-----------\n\n - ID - just an identifier (not part of the features)\n - CNNs - 4096 dimensional Convolutional Neural Networks (CNNs) features\n - GIST - 512 dimensional GIST features\n - prediction - label 1 for sunny and label 0 for not sunny.", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Description", "content": "This is the sunny v. not sunny binary classification competition for G6061 Undergraduate and 934G5 Postgraduate Machine Learning Module spring teaching 2018/2019 at the University of Sussex, UK. Please make an account with your University of Sussex email ID.\n\nYou are provided with 259 labelled training data (146 of sunny scenes and 113 of not sunny scenes), and 2818 of test data, which are not labelled. The task is to develop a binary-class classifier that predicts the labels for the test data set. Each data instance is represented as a 4608 dimensional feature vector. This vector is a concatenation of 4096 dimensional deep Convolutional Neural Networks (CNNs) features extracted from the fc7 activation layer of CaffeNet and 512 dimensional GIST features (**this representation is given therefore you do not need to perform any feature extraction on images**).\n\nMight be interesting to note that our training data were collected in the seaside area, while test data were collected in the urban area. \n![enter image description here][1]\n\n**Additionally**, you are also provided with three types of information that might be useful when building your classifier: a) additional 2331 labelled training data which is incomplete as it has missing feature values, b) confidence of the label annotation for each training data point (259 labelled training data and additional but incomplete 2331 labelled training data), and c) the proportion of positive (sunny) data points and the proportion of not sunny data points in the test set. You can choose to incorporate or to ignore these additional data.\n\nYou can use any of your favourite classifiers. In this module, we have discussed: perceptron, multi-layer perceptron, random forest (G6061), RBF networks (934G5 PG), support vector machine, logistic regression, and their kernelized versions (934G5 PG).\n\nThere are **2 leaderboards** - one public that is 25% of the test data and one private that is the other 75%. Public-private splits are done randomly. Public leaderboard will be used to evaluate your current score and ranking till deadline date. The final rankings will be based on the private leaderboard - so make sure you do not overfit your model to the public leader board. Keep in mind that you can upload multiple prediction files; your only limit is that at most 5 prediction files can be uploaded per day. You can select up to 2 final submissions for judging. You have to make at least one submission to this competition! Format of the solution file you submit should be same as the file sample_valid_submission.csv (i.e. 2 columns with 1st column as ID & 2nd column as prediction). With each submission, please write a brief description of the model (e.g. logistic regression with the regularisation parameter=10).\n\n**Timing**: - This competition will be closed on Friday 14 June 2019 11:59PM. - Check our e-submission system for the deadline of the report.\n\n [1]: http://users.sussex.ac.uk/~nq28/teaser.png", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Evaluation", "content": "The evaluation metric for this competition is **Classification Accuracy**.\n\nSubmission Format\n\nA comma-separated value (csv) submission file should contain two columns: ID and prediction. The ID should match the ID on your test data (it is from 1 to 2818).\n\nThe file should contain a header and have the following format:\n\n ID,prediction\n 1,1\n 2,0\n 3,1\n 4,1\n etc.", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false } ] }