"description":"Logistic regression with MapReduce\r\n\r\nAlgorithm builds a model with continuous features and predicts binary target variable (1, 0). Learning is done by fitting theta parameters to the training data where the likelihood function is optimized by using Newton-Raphson to update theta parameters. The output of algorithm is consistent with implementation of logistic regression classifier in Orange.\r\n\r\nReference:\r\nMapReduce version of algorithm is proposed by Cheng-Tao Chu; Sang Kyun Kim, Yi-An Lin, YuanYuan Yu, Gary Bradski, Andrew Ng, and Kunle Olukotun. \"Map-Reduce for Machine Learning on Multicore\". NIPS 2006. ",
"static_image":"",
"action":"logreg_fit",
"visualization_view":"",
"streaming_visualization_view":"",
"post_interact_action":"",
"wsdl_method":"",
"wsdl":"",
"interactive":false,
"windows_queue":false,
"order":5,
"name":"Logistic regression"
}
},
{
"model":"workflows.abstractinput",
"fields":{
"widget":"16042741-6834-43ab-9587-b64a1f460238",
"name":"Dataset",
"short_name":"dst",
"default":"",
"description":"",
"required":true,
"multi":false,
"parameter_type":null,
"variable":"dataset",
"parameter":false,
"order":1,
"uid":"29dd4a35-7639-493f-9e7b-a64f54f0d06d"
}
},
{
"model":"workflows.abstractinput",
"fields":{
"widget":"16042741-6834-43ab-9587-b64a1f460238",
"name":"Convergence",
"short_name":"con",
"default":"1e-8",
"description":"The value defines the convergence of the logistic regression.",
"required":true,
"multi":false,
"parameter_type":"text",
"variable":"alpha",
"parameter":true,
"order":2,
"uid":"104f5edc-6029-4ae8-8a83-29557fa901fd"
}
},
{
"model":"workflows.abstractinput",
"fields":{
"widget":"16042741-6834-43ab-9587-b64a1f460238",
"name":"Max. number of iterations",
"short_name":"itr",
"default":"10",
"description":"Define a maximum number of iterations. If the cost function converges it will stop sooner.",
"description":"Multiple URLs can be specified. An URL should be accessible via HTTP and not HTTPS. ",
"required":true,
"multi":false,
"parameter_type":"textarea",
"variable":"url",
"parameter":true,
"order":1,
"uid":"e2883c3d-7b3a-46ec-8673-10da5f494ec9"
}
},
{
"model":"workflows.abstractinput",
"fields":{
"widget":"189c6a1b-612a-4ca6-a7e3-c39349922781",
"name":"URL range",
"short_name":"rng",
"default":"",
"description":"The URL range parameter is used with URLs that point to file chunks, named as xaaaa to xzzzz. This naming is provided by the unix split command. The first and last URL should be defined in the URLs text box. Intermediate URLs will be automatically generated.",
"required":false,
"multi":false,
"parameter_type":"checkbox",
"variable":"range",
"parameter":true,
"order":2,
"uid":"992a24e0-0365-46ef-8dd2-37fa19563bd5"
}
},
{
"model":"workflows.abstractinput",
"fields":{
"widget":"189c6a1b-612a-4ca6-a7e3-c39349922781",
"name":"Gzipped data",
"short_name":"zip",
"default":"",
"description":"Select if specified URLs point to data in gzipped format.",
"required":false,
"multi":false,
"parameter_type":"checkbox",
"variable":"data_type",
"parameter":true,
"order":3,
"uid":"d62fc126-afda-4f0b-876d-5180cd409779"
}
},
{
"model":"workflows.abstractinput",
"fields":{
"widget":"189c6a1b-612a-4ca6-a7e3-c39349922781",
"name":"Attribute selection",
"short_name":"ind",
"default":"",
"description":"Select attributes that will processed. \r\n\r\nExample: 1 - 10 for indices in the range from 1 to 10 or 1,2 for indices 1 and 2.",
"required":true,
"multi":false,
"parameter_type":"text",
"variable":"X_indices",
"parameter":true,
"order":4,
"uid":"e32184e0-f38a-466f-a991-82e084a1cd8b"
}
},
{
"model":"workflows.abstractinput",
"fields":{
"widget":"189c6a1b-612a-4ca6-a7e3-c39349922781",
"name":"Attribute metadata",
"short_name":"mta",
"default":"numeric",
"description":"Select numeric, if all attributes are numeric or discrete, if all attributes are discrete. \r\n",
"required":true,
"multi":false,
"parameter_type":"select",
"variable":"atr_meta",
"parameter":true,
"order":5,
"uid":"705b4459-6f05-4fd3-a230-33e0ddd784b3"
}
},
{
"model":"workflows.abstractinput",
"fields":{
"widget":"189c6a1b-612a-4ca6-a7e3-c39349922781",
"name":"Metadata URL",
"short_name":"cmt",
"default":"",
"description":"Define an URL of a file with attribute metadata.\r\n\r\nExample of a file with 3 attributes, where first and second are continous and third is discrete:\r\natr1, atr2, atr3\r\nc,c,d ",
"required":false,
"multi":false,
"parameter_type":"text",
"variable":"custom",
"parameter":true,
"order":6,
"uid":"491eab89-b3f1-4503-864b-7cad9a01dda7"
}
},
{
"model":"workflows.abstractinput",
"fields":{
"widget":"189c6a1b-612a-4ca6-a7e3-c39349922781",
"name":"ID index",
"short_name":"id",
"default":"",
"description":"Define identifier index in the data.",
"required":false,
"multi":false,
"parameter_type":"text",
"variable":"id_index",
"parameter":true,
"order":7,
"uid":"f100a206-8fdd-4f8f-afa4-c7d3c3b6f0ca"
}
},
{
"model":"workflows.abstractinput",
"fields":{
"widget":"189c6a1b-612a-4ca6-a7e3-c39349922781",
"name":"Class index",
"short_name":"tar",
"default":"",
"description":"Define the class index in the dataset. If it is not defined, last attribute is used as the class.",
"required":false,
"multi":false,
"parameter_type":"text",
"variable":"y_index",
"parameter":true,
"order":8,
"uid":"69a3061b-9dcf-4c18-9f69-b5bc082dd65e"
}
},
{
"model":"workflows.abstractinput",
"fields":{
"widget":"189c6a1b-612a-4ca6-a7e3-c39349922781",
"name":"Delimiter",
"short_name":"dlt",
"default":",",
"description":"Define delimiter to parse the data.",
"required":false,
"multi":false,
"parameter_type":"text",
"variable":"delimiter",
"parameter":true,
"order":9,
"uid":"fd2eff8a-b2d0-4de6-9384-13ce5385e3fc"
}
},
{
"model":"workflows.abstractinput",
"fields":{
"widget":"189c6a1b-612a-4ca6-a7e3-c39349922781",
"name":"Missing values",
"short_name":"mv",
"default":"",
"description":"Missing data values are skipped.\r\n\r\nExample: ?,",
"required":false,
"multi":false,
"parameter_type":"text",
"variable":"missing_vals",
"parameter":true,
"order":10,
"uid":"f393261f-ba93-4ed5-bdfc-82f6027bb327"
}
},
{
"model":"workflows.abstractinput",
"fields":{
"widget":"189c6a1b-612a-4ca6-a7e3-c39349922781",
"name":"Class mapping",
"short_name":"map",
"default":"",
"description":"The class mapping defines a mapping for a binary class. It is used with Logistic regression and Linear SVM.\r\n\r\nThe Logistic regression classifier uses 0 and 1 as class. If the dataset contains discrete classes (e.g. healthy, sick), a mapping should be defined, where healthy is mapped to 1 and sick to 0. The class mapping is used only with binary target labels.\r\nExample: healthy, sick",
"description":"Naive Bayes with MapReduce\r\n\r\nAlgorithm calculates multinomial distribution for discrete features and Gaussian distribution for numerical features. The output of algorithm is consistent with implementation of Naive Bayes classifier in Orange and scikit-learn.\r\n\r\nReference:\r\nMapReduce version of algorithm is proposed by Cheng-Tao Chu; Sang Kyun Kim, Yi-An Lin, YuanYuan Yu, Gary Bradski, Andrew Ng, and Kunle Olukotun. \"Map-Reduce for Machine Learning on Multicore\". NIPS 2006. ",
