You cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts.
\nYou must use only Decision Tree as your model.
\n\n\n Privately sharing code or data outside of teams is not permitted.\n
\n\nYour team name must be your student ID.
\n\nYou may submit a maximum of 3 entries per day.
\nYou may select up to 1 final submissions for judging.
\n\nEnd Date: 11/19/2018
\n\t", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "data-description", "content": "This dataset contains incidents derived from SFPD Crime Incident Reporting system. The data ranges from 1/1/2003 to 5/13/2015. The training set and test set rotate every week, meaning week 1,3,5,7... belong to test set, week 2,4,6,8 belong to training set. \n\nThe classification goal is to predict the category of crime that occurred.\nFile descriptions\n-----------------\n - **crime_train.csv** - the training set \n - **crime_test.csv** - the test set\n - **crime_sample_submission.csv** - a sample submission file in the correct format. **Note that the prediction should be a probability for each category instead of one hot scheme (e.g., [0.1, 0.6, 0.3], not [0, 1, 0] for three categories).**\n\n## Data fields ##\n\n- **Dates**: timestamp of the crime incident\n- **Category**: category of the crime incident (only in train.csv). **This is the target variable you are going to predict.**\n- **Descript**: detailed description of the crime incident (only in train.csv)\n- **DayOfWeek**: the day of the week\n- **PdDistrict**: name of the Police Department District\n- **Resolution**: how the crime incident was resolved (only in train.csv)\n- **Address**: the approximate street address of the crime incident \n- **X**: Longitude\n- **Y**: Latitude", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Description", "content": "From 1934 to 1963, San Francisco was infamous for housing some of the world's most notorious criminals on the inescapable island of Alcatraz.\n\nToday, the city is known more for its tech scene than its criminal past. But, with rising wealth inequality, housing shortages, and a proliferation of expensive digital toys riding BART to work, there is no scarcity of crime in the city by the bay.\n\nFrom Sunset to SOMA, and Marina to Excelsior, this competition's dataset provides nearly 12 years of crime reports from across all of San Francisco's neighborhoods. Given time and location, you must predict the category of crime that occurred.\n\n\n----------\n\n\n***Please note: Only provided training data must be used to train your decision tree.***\n\nSubmissions are evaluated using the multi-class logarithmic loss. Each incident has been labeled with one true class. For each incident, you must submit a set of predicted probabilities (one for every class).
\n\nThe formula is then,
\n\n\nwhere \\(N\\) is the number of cases in the test set, \\(M\\) is the number of class labels, \\(log\\) is the natural logarithm, \\( y_{i,j} \\) is 1 if observation \\(i\\) is in class \\(j\\) and 0 otherwise, and \\( p_{i,j} \\) is the predicted probability that observation \\(i\\) belongs to class \\(j\\).
\n\nSample submission format is uploaded in Data tab. Note that the prediction should be a probability for each category instead of one hot scheme (e.g., [0.1, 0.6, 0.3], not [0, 1, 0] for three categories).
\n\n", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false } ] }