"description":"Select equal frequency discretization or random discretization for numeric attributes",
"name":"Discretization accuracy",
"short_name":"dac",
"default":"1",
"description":"Continuous attributes are converted to discrete intervals. For exact estimation use 0 (slowest) or increase the number to get an approximation (faster).",
"required":true,
"multi":false,
"parameter_type":"select",
"variable":"split_fun",
"parameter_type":"text",
"variable":"accuracy",
"parameter":true,
"order":7,
"order":8,
"uid":"00758cdf-2eb5-43c5-bedf-bd3b8b9c29d6"
}
},
{
"model":"workflows.abstractinput",
"fields":{
"widget":"4f2ce923-62e6-4be1-a394-72ac52988386",
"name":"Separate most represented class",
"short_name":"smp",
"default":"true",
"description":"separate_max",
"required":true,
"multi":false,
"parameter_type":"checkbox",
"variable":"separate_max",
"parameter":true,
"order":9,
"uid":"21444978-142f-4f3d-947c-20e0b41a2c9b"
}
},
{
"model":"workflows.abstractinput",
"fields":{
"widget":"4f2ce923-62e6-4be1-a394-72ac52988386",
"name":"Min samples in leaf",
"short_name":"msl",
"default":"5",
"description":"The minimum number of samples in newly created leaves. A split is discarded if after the split, one of the leaves would contain less then min samples leaf samples",
"required":true,
"multi":false,
"parameter_type":"text",
"variable":"min_samples_leaf",
"parameter":true,
"order":5,
"uid":"52591706-7f30-4def-a788-3e07d3f82876"
}
},
{
"model":"workflows.abstractinput",
"fields":{
"widget":"4f2ce923-62e6-4be1-a394-72ac52988386",
"name":"Max tree nodes",
"short_name":"mnt",
"default":"20",
"default":"100",
"description":"Max. number of decision tree nodes",
"required":true,
"multi":false,
...
...
@@ -88,7 +122,7 @@
"parameter_type":"select",
"variable":"measure",
"parameter":true,
"order":6,
"order":7,
"uid":"68cbccf9-7469-4b55-b96e-4f7c6a3c9cde"
}
},
...
...
@@ -105,7 +139,7 @@
"parameter_type":"text",
"variable":"seed",
"parameter":true,
"order":8,
"order":10,
"uid":"8e6e2d96-3457-4b23-ac93-ab90b083920f"
}
},
...
...
@@ -132,7 +166,7 @@
"widget":"4f2ce923-62e6-4be1-a394-72ac52988386",
"name":"Min samples split",
"short_name":"lmi",
"default":"5",
"default":"10",
"description":"Min. number of samples to split the node",
"description":"Number of randomly chosen medoids to calculate similaty.",
"required":true,
"multi":false,
"parameter_type":"text",
"variable":"num_medoids",
"parameter":true,
"order":4,
"uid":"1bbcbc2c-a9d5-4427-a8ef-e4dd58c22f86"
}
},
{
"model":"workflows.abstractinput",
"fields":{
"widget":"72a39fab-5433-493f-ae22-12a264075356",
"name":"Separate most represented class",
"short_name":"smp",
"default":"true",
"description":"",
"required":true,
"multi":false,
"parameter_type":"checkbox",
"variable":"separate_max",
"parameter":true,
"order":10,
"uid":"2ccff5c1-7e06-4887-863d-7acf76209e50"
}
},
{
"model":"workflows.abstractinput",
"fields":{
...
...
@@ -54,7 +88,7 @@
"parameter_type":"text",
"variable":"seed",
"parameter":true,
"order":8,
"order":11,
"uid":"31c68e34-3bff-41bb-bf77-925c6171a6f6"
}
},
...
...
@@ -75,13 +109,30 @@
"uid":"37879268-0aa9-4458-afb2-71a521acb299"
}
},
{
"model":"workflows.abstractinput",
"fields":{
"widget":"72a39fab-5433-493f-ae22-12a264075356",
"name":"Min samples in leaf",
"short_name":"msl",
"default":"5",
"description":"The minimum number of samples in newly created leaves. A split is discarded if after the split, one of the leaves would contain less then min samples leaf samples",
"required":true,
"multi":false,
"parameter_type":"text",
"variable":"min_samples_leaf",
"parameter":true,
"order":6,
"uid":"3a893a69-f22e-448b-9a92-222573c655ba"
}
},
{
"model":"workflows.abstractinput",
"fields":{
"widget":"72a39fab-5433-493f-ae22-12a264075356",
"name":"Max tree nodes",
"short_name":"mnt",
"default":"20",
"default":"100",
"description":"Max. number of decision tree nodes",
"required":true,
"multi":false,
...
...
@@ -92,6 +143,23 @@
"uid":"3d48b0d0-a304-45d5-9d18-3ca17e8fcf05"
}
},
{
"model":"workflows.abstractinput",
"fields":{
"widget":"72a39fab-5433-493f-ae22-12a264075356",
"name":"Discretization accuracy",
"short_name":"dac",
"default":"1",
"description":"Continuous attributes are converted to discrete intervals. For exact estimation use 0 (slowest) or increase the number to get an approximation (faster).",
"required":true,
"multi":false,
"parameter_type":"text",
"variable":"accuracy",
"parameter":true,
"order":9,
"uid":"3ff0f040-3d11-413f-975a-1fde57bf289b"
}
},
{
"model":"workflows.abstractinput",
"fields":{
...
...
@@ -122,7 +190,7 @@
"parameter_type":"select",
"variable":"measure",
"parameter":true,
"order":6,
"order":8,
"uid":"9a8f3c2c-265c-4b37-93c1-d58fee9dd7af"
}
},
...
...
@@ -132,34 +200,17 @@
"widget":"72a39fab-5433-493f-ae22-12a264075356",
"name":"Min samples split",
"short_name":"lmi",
"default":"5",
"default":"10",
"description":"Min. number of samples to split the node",
"required":true,
"multi":false,
"parameter_type":"text",
"variable":"leaf_min_inst",
"parameter":true,
"order":4,
"order":5,
"uid":"ac032f38-f4a4-44ea-8c02-96506d4f8e86"
}
},
{
"model":"workflows.abstractinput",
"fields":{
"widget":"72a39fab-5433-493f-ae22-12a264075356",
"name":"Discretization",
"short_name":"spf",
"default":"equal_freq",
"description":"Select equal frequency discretization or random discretization for numeric attributes",
"description":"Random forest calculates difference in probability between most and second most probable prediction. If difference is greater than parameter diff, it outputs prediction. If a test sample is hard to predict (difference is never higher than diff), it queries whole ensemble to make a prediction.",
"name":"Weighted forest - similarity coeff",
"short_name":"coe",
"default":"0.5",
"description":"Percentage of most similar treees to include in prediction (0 - 1)",