\nThis page has the datafiles that you will be using to do your training and testing for the data. \n
\n- train.csv has your training data\n - Class 0 is <= 50K\n - Class 1 is >50K \n- test.csv has your testing data which you will use to make your predictions for submission\n", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Description", "content": "If you joined this competition it means that you chose to work with the adult dataset, instead of the zoo dataset. Your goal now will be to take the training data and use it to train a KNN and Naive Bayes Classifiers. After training, make predictions and then format your results similar to what is in the example solution file, and submit them. The final submission you make will be counted as your submission for this portion of the assignment.
\n\nTest and training data can be read in using the code below. Similar to your in-class assignments, make sure the data files are in the same folder as your notebook file.
\n\n\ndata_train_df = pd.read_csv(\"TrainingFileName.csv\")\ndata_train_ft = data_train_df.drop('Class', axis=1)\ndata_train_tgt = data_train_df[\"Class\"]\n\n\n\nTo format your solution correctly you can use the function below. It will take your prediction data and write it to CSV file, which you can then submit as your solution.
\n\n//predictions should be the result returned by modelName.fit(test_features)\ndef writeSubmission(predictions):\n i=1\n submissionList = []\n for prediction in predictions:\n submissionList.append([str(i), str(prediction)])\n i+=1\n \n with open('submission.csv', 'w', newline='') as submission:\n writer = csv.writer(submission)\n\n writer.writerow(['Id', 'Category'])\n for row in submissionList:\n writer.writerow(row)\n\n",
"isPublished": false,
"hasIsPublished": false,
"postTitle": "",
"hasPostTitle": false,
"mimeType": "",
"hasMimeType": false
},
{
"name": "Evaluation",
"content": "The evaluation pages describes how submissions will be scored and how students should format their submissions. You don't need a sub-title at the top (the page title appears above). For an example of a high quality evaluation page, see the 2013 KDD Cup page, which we have copied here:
\r\nThe evaluation metric for this competition is Mean F1-Score. The F1 score, commonly used in information retrieval, measures accuracy using the statistics precision p and recall r. Precision is the ratio of true positives (tp) to all predicted positives (tp + fp). Recall is the ratio of true positives to all actual positives (tp + fn). The F1 score is given by:
\r\n\\[ F1 = 2\\frac{p \\cdot r}{p+r}\\ \\ \\mathrm{where}\\ \\ p = \\frac{tp}{tp+fp},\\ \\ r = \\frac{tp}{tp+fn} \\]
\r\nThe F1 metric weights recall and precision equally, and a good retrieval algorithm will maximize both precision and recall simultaneously. Thus, moderately good performance on both will be favored over extremely good performance on one and poor performance on the other.
\r\nFor every author in the dataset, submission files should contain two columns: AuthorId and DuplicateAuthorIds. DuplicateAuthorIds should be a space-delimited list. Every AuthorId counts as his/her own duplicate, and every duplicate should be listed under each of its respective ids. For example, if you suspect author A, B, and C are the same, you should list (A,A B C), (B,B A C), (C,C A B).
\r\nThe file should contain a header and have the following format:
\r\nAuthorId,DuplicateAuthorIds", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false } ] }
1,1
8,8
9,9 10
10,10 9
etc.