boronhub

{
  "citation": "\n@misc{Dua:2019 ,\nauthor = \"Dua, Dheeru and Graff, Casey\",\nyear = \"2017\",\ntitle = \"{UCI} Machine Learning Repository\",\nurl = \"http://archive.ics.uci.edu/ml\",\ninstitution = \"University of California, Irvine, School of Information and Computer Sciences\" }\n",
  "description": "\nThis database encodes the complete set of possible board configurations at the end of tic-tac-toe games, where \"x\" is assumed to have played first. The target concept is \"win for x\" (i.e., true when \"x\" has one of 8 possible ways to create a \"three-in-a-row\").\n\nInterestingly, this raw database gives a stripped-down decision tree algorithm (e.g., ID3) fits. However, the rule-based CN2 algorithm, the simple IB1 instance-based learning algorithm, and the CITRE feature-constructing decision tree algorithm perform well on it.\n",
  "location": {
    "urls": [
      "https://archive.ics.uci.edu/ml/datasets/Tic-Tac-Toe+Endgame"
    ]
  },
  "name": "tic_tac_toe",
  "schema": {

boronhub

{
  "citation": "\n@misc{Dua:2019 ,\nauthor = \"Dua, Dheeru and Graff, Casey\",\nyear = \"2017\",\ntitle = \"{UCI} Machine Learning Repository\",\nurl = \"http://archive.ics.uci.edu/ml\",\ninstitution = \"University of California, Irvine, School of Information and Computer Sciences\" }\n",
  "description": "\nThe objective is to identify each of a large number of black-and-white rectangular pixel displays as one of the 26 capital letters in the English alphabet.\nThe character images were based on 20 different fonts and each letter within these 20 fonts was randomly distorted to produce a file of 20,000 unique stimuli. Each stimulus was converted into 16 primitive numerical attributes (statistical moments and edge counts) which were then scaled to fit into a range of integer values from 0 through 15. We typically train on the first 16000 items and then use the resulting model to predict the letter category for the remaining 4000. See the article cited above for more details.\n",
  "location": {
    "urls": [