(Rudy: cause it might not be fun to do ML in JavaScript, but we just have to keep at it)
###Example data Some data for illustration
var test_set = [{"id": 1, "target": 1, "input": 1, "input2": 2},
{"id": 2, "target": 1, "input": 2, "input2": 3},
{"id": 3, "target": 1, "input": 3, "input2": 4},
{"id": 4, "target": 1, "input": 3, "input2": 4},
{"id": 5, "target": 0, "input": 5, "input2": 5},
{"id": 6, "target": 0, "input": 6, "input2": 6},
{"id": 7, "target": 0, "input": 7, "input2": 7},
{"id": 8, "target": 0, "input": 5, "input2": 8},
{"id": 9, "target": 0, "input": 4, "input2": 3},
{"id": 10, "target": 0, "input": 3, "input2": 5},
{"id": 10, "target": 1, "input": 3, "input2": 6},
{"id": 10, "target": 1, "input": 2, "input2": 8},
{"id": 11, "target": 0, "input": 7, "input2": 9},
{"id": 12, "target": 0, "input": 6, "input2": 0},
{"id": 13, "target": 0, "input": 7, "input2": 4}
];
##Basic functionality - basics.js ###tab() - basic cross tabs
- v1: given an array and user-specified attributes, returns an array of cross-tabs.
tab(test_set,"input","target")
###summary() - in-development
###unique() - unique values in list
##Supervised Learning - rudyTree.js ###binaryTree() - for discrete tree learning v1: A easy to use decision tree for binary targets and numeric input variables. v2: In development will support discrete input variables and provide facility for ROC curves
inputs = ["input","input2"];
output = binaryTree(test_set,inputs,"target",5)
###ROC() - Receiving-Operating Characteristics Curve
##Unsupervised Learning - rudyKmeans.js ###kmeans() -