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## metadata.json
{
  "name": "Incremental anomalies",
  "kind": "script",
  "description": "Adds a new dataset to a collection and computes a set of anomalies for the new, extended data",
  "source_code": "script.whizzml",
  "imports":[
    ],
  "inputs":[
    {
      "name": "url",

## JSON PML schemas.md

      
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                aficionado
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                JSON PML schemas 
              
          
    JSON PML: schemas for models

model-schema.json A generic ML model, containing fields shared by most models despite of their concrete type.  It uses:

sample-schema.json The schema for dataset sampling specifications
field-collection-schema.json Auxiliary schema describing a collection of field (or "properties") descriptors
generic-field-schema.json Properties shared by all fields, regardless of their type.
field-schema.json The union schema of all field descriptor types, with their specific properties.


tree-model-schema.json A specialization of the model schema to decision tree models.  It uses:
node-schema The schema for the nodes in a decision tree


## metadata.json
{
    "name": "Merge two datasets",
    "description": "Merges two different datasets",
    "source_code": "script.whizzml",
    "inputs": [
        {
            "name": "dataset-1",
            "type": "dataset-id",
            "description": "First dataset"
        },

## metadata.json
{
  "name": "Tree optimization",
  "description": "Script for tree optimization using SMACdown",
  "kind": "script",
  "source_code": "script.whizzml",
  "inputs":[
    {
      "name": "dataset-id",
      "type": "dataset-id",
      "description": "Dataset for which we are seeking an optimal tree"

## example.py
#!/usr/bin/env python
"""A WhizzML simple example

"""

from bigml.api import BigML

API = BigML()

LIBRARY = API.create_library("(define (addition a b) (+ a b))")

## gradient-boosting.whizzml
;; This is a vanilla implementation of gradient boosting.  The main
;; function is at the bottom of the script, where it explains the
;; algorithm in some detail.

;; A constant added to the generated field names to let us know that
;; we generated them
(define boost-id "__bmlboost")

;; The names of the fields contain ground truth - if there are k
;; classes, this is k coluns, one for each class.  If the true class

## model-or-ensemble.json
{
    "name": "Model or ensemble",
    "description": "Select the best option for modeling a source: a model or an ensemble?",
    "parameters": [
        {
            "name": "input-source-id",
            "type": "source-id",
            "description": "Source for training/test the model and ensemble"
        }
    ],

## one-click-dataset.json
{
  "name": "One-Click Dataset",
  "description": "Create a new dataset from a source with a click",
  "outputs": [
    {"name": "dataset-id", "type": "dataset-id", "description": "The new dataset"},
    {"name": "rows", "type": "number", "description": "The number of rows of the new dataset"}
  ],
  "parameters": [
    {"name": "source-id", "type": "source-id", "default": "", "description": "Source from which to create a new dataset"},
    {"name": "source-name", "type": "string", "default": "", "description": "Name for the new dataset"}

## index.html
<!DOCTYPE html>
<meta charset="utf-8">
<style>
html, body{
  height: 100%;
}
*, *:before, *:after {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;

## index.html
<!doctype html>
<html lang="en">

<head>
    <meta charset="utf-8">
    <meta http-equiv="X-UA-Compatible" content="IE=edge,chrome=1">
    <title>BigML Prediction Test</title>
    <meta name="description" content="">
    <meta name="viewport" content="width=device-width, initial-scale=1, maximum-scale=1">
    <script src="http://code.jquery.com/jquery-1.7.1.js" type="text/javascript"></script>
	{
	"name": "Incremental anomalies",
	"kind": "script",
	"description": "Adds a new dataset to a collection and computes a set of anomalies for the new, extended data",
	"source_code": "script.whizzml",
	"imports":[
	],
	"inputs":[
	{
	"name": "url",
	{
	"name": "Merge two datasets",
	"description": "Merges two different datasets",
	"source_code": "script.whizzml",
	"inputs": [
	{
	"name": "dataset-1",
	"type": "dataset-id",
	"description": "First dataset"
	},
	{
	"name": "Tree optimization",
	"description": "Script for tree optimization using SMACdown",
	"kind": "script",
	"source_code": "script.whizzml",
	"inputs":[
	{
	"name": "dataset-id",
	"type": "dataset-id",
	"description": "Dataset for which we are seeking an optimal tree"
	#!/usr/bin/env python
	"""A WhizzML simple example

	"""

	from bigml.api import BigML

	API = BigML()

	LIBRARY = API.create_library("(define (addition a b) (+ a b))")
	;; This is a vanilla implementation of gradient boosting. The main
	;; function is at the bottom of the script, where it explains the
	;; algorithm in some detail.

	;; A constant added to the generated field names to let us know that
	;; we generated them
	(define boost-id "__bmlboost")

	;; The names of the fields contain ground truth - if there are k
	;; classes, this is k coluns, one for each class. If the true class
	{
	"name": "Model or ensemble",
	"description": "Select the best option for modeling a source: a model or an ensemble?",
	"parameters": [
	{
	"name": "input-source-id",
	"type": "source-id",
	"description": "Source for training/test the model and ensemble"
	}
	],
	{
	"name": "One-Click Dataset",
	"description": "Create a new dataset from a source with a click",
	"outputs": [
	{"name": "dataset-id", "type": "dataset-id", "description": "The new dataset"},
	{"name": "rows", "type": "number", "description": "The number of rows of the new dataset"}
	],
	"parameters": [
	{"name": "source-id", "type": "source-id", "default": "", "description": "Source from which to create a new dataset"},
	{"name": "source-name", "type": "string", "default": "", "description": "Name for the new dataset"}
	<!DOCTYPE html>
	<meta charset="utf-8">
	<style>
	html, body{
	height: 100%;
	}
	, :before, *:after {
	-webkit-box-sizing: border-box;
	-moz-box-sizing: border-box;
	box-sizing: border-box;
	<!doctype html>
	<html lang="en">

	<head>
	<meta charset="utf-8">
	<meta http-equiv="X-UA-Compatible" content="IE=edge,chrome=1">
	<title>BigML Prediction Test</title>
	<meta name="description" content="">
	<meta name="viewport" content="width=device-width, initial-scale=1, maximum-scale=1">
	<script src="http://code.jquery.com/jquery-1.7.1.js" type="text/javascript"></script>