159 instantiations of TGUI
Microsoft.ML.Data (2)
Transforms\NormalizeColumn.cs (2)
110
[
TGUI
(Label = "Max number of bins")]
233
[
TGUI
(Label = "Max number of bins")]
Microsoft.ML.Ensemble (33)
OutputCombiners\BaseStacking.cs (1)
22
[
TGUI
(Label = "Validation Dataset Proportion")]
OutputCombiners\MultiStacking.cs (1)
47
[
TGUI
(Label = "Base predictor")]
OutputCombiners\MultiWeightedAverage.cs (3)
48
[
TGUI
(Label = "Metric Name", Description = "The weights are calculated according to the selected metric")]
100
[
TGUI
(Label = MulticlassClassificationEvaluator.AccuracyMicro)]
102
[
TGUI
(Label = MulticlassClassificationEvaluator.AccuracyMacro)]
OutputCombiners\RegressionStacking.cs (1)
45
[
TGUI
(Label = "Base predictor")]
OutputCombiners\Stacking.cs (1)
44
[
TGUI
(Label = "Base predictor")]
OutputCombiners\WeightedAverage.cs (7)
43
[
TGUI
(Label = "Weightage Name", Description = "The weights are calculated according to the selected metric")]
98
[
TGUI
(Label = BinaryClassifierEvaluator.Accuracy)]
100
[
TGUI
(Label = BinaryClassifierEvaluator.Auc)]
102
[
TGUI
(Label = BinaryClassifierEvaluator.PosPrecName)]
104
[
TGUI
(Label = BinaryClassifierEvaluator.PosRecallName)]
106
[
TGUI
(Label = BinaryClassifierEvaluator.NegPrecName)]
108
[
TGUI
(Label = BinaryClassifierEvaluator.NegRecallName)]
Selector\SubModelSelector\BestDiverseSelectorBinary.cs (1)
29
[
TGUI
(Label = "Diversity Measure Type")]
Selector\SubModelSelector\BestDiverseSelectorMulticlass.cs (1)
30
[
TGUI
(Label = "Diversity Measure Type")]
Selector\SubModelSelector\BestDiverseSelectorRegression.cs (1)
29
[
TGUI
(Label = "Diversity Measure Type")]
Selector\SubModelSelector\BestPerformanceRegressionSelector.cs (1)
25
[
TGUI
(Label = "Metric Name")]
Selector\SubModelSelector\BestPerformanceSelector.cs (1)
25
[
TGUI
(Label = "Metric Name")]
Selector\SubModelSelector\BestPerformanceSelectorMulticlass.cs (1)
25
[
TGUI
(Label = "Metric Name")]
Selector\SubModelSelector\SubModelDataSelector.cs (2)
18
[
TGUI
(Label = "Learners Selection Proportion")]
23
[
TGUI
(Label = "Validation Dataset Proportion")]
Trainer\Binary\EnsembleTrainer.cs (2)
42
[
TGUI
(Label = "Sub-Model Selector(pruning) Type",
47
[
TGUI
(Label = "Output combiner", Description = "Output combiner type")]
Trainer\EnsembleTrainerBase.cs (5)
30
[
TGUI
(Label = "Number of Models per batch")]
34
[
TGUI
(Label = "Batch Size",
40
[
TGUI
(Label = "Sampling Type", Description = "Subset Selection Algorithm to induce the base learner.Sub-settings can be used to select the features")]
44
[
TGUI
(Label = "Train parallel", Description = "All the base learners will run asynchronously if the value is true")]
50
[
TGUI
(Label = "Show Sub-Model Metrics")]
Trainer\Multiclass\MulticlassDataPartitionEnsembleTrainer.cs (2)
44
[
TGUI
(Label = "Sub-Model Selector(pruning) Type", Description = "Algorithm to prune the base learners for selective Ensemble")]
48
[
TGUI
(Label = "Output combiner", Description = "Output combiner type")]
Trainer\Regression\RegressionEnsembleTrainer.cs (2)
38
[
TGUI
(Label = "Sub-Model Selector(pruning) Type", Description = "Algorithm to prune the base learners for selective Ensemble")]
42
[
TGUI
(Label = "Output combiner", Description = "Output combiner type")]
Microsoft.ML.FastTree (34)
FastTreeArguments.cs (29)
70
[
TGUI
(Label = "Optimize for unbalanced")]
228
[
TGUI
(NoSweep = true)]
243
[
TGUI
(NotGui = true)]
251
[
TGUI
(NotGui = true)]
256
[
TGUI
(NotGui = true)]
261
[
TGUI
(NotGui = true)]
266
[
TGUI
(NotGui = true)]
271
[
TGUI
(NotGui = true)]
380
[
TGUI
(NotGui = true)]
514
[
TGUI
(Description = "The maximum number of leaves per tree", SuggestedSweeps = "2-128;log;inc:4")]
524
[
TGUI
(Description = "Minimum number of training instances required to form a leaf", SuggestedSweeps = "1,10,50")]
533
[
TGUI
(Description = "Total number of trees constructed", SuggestedSweeps = "20,100,500")]
576
[
TGUI
(NotGui = true)]
584
[
TGUI
(NotGui = true)]
591
[
TGUI
(NotGui = true)]
599
[
TGUI
(NotGui = true)]
608
[
TGUI
(NotGui = true)]
695
[
TGUI
(Label = "Early Stopping Rule", Description = "Early stopping rule. (Validation set (/valid) is required.)")]
722
[
TGUI
(Description = "Early stopping metrics. (For regression, 1: L1, 2:L2; for ranking, 1:NDCG@1, 3:NDCG@3)")]
741
[
TGUI
(Description = "Pruning threshold")]
748
[
TGUI
(Description = "Pruning window size")]
755
[
TGUI
(Label = "Learning Rate", SuggestedSweeps = "0.025-0.4;log")]
763
[
TGUI
(Label = "Shrinkage", SuggestedSweeps = "0.25-4;log")]
771
[
TGUI
(SuggestedSweeps = "0,0.000000001,0.05,0.1,0.2")]
797
[
TGUI
(NotGui = true)]
804
[
TGUI
(NotGui = true)]
827
[
TGUI
(NotGui = true)]
835
[
TGUI
(NotGui = true)]
844
[
TGUI
(NotGui = true)]
GamClassification.cs (1)
70
[
TGUI
(Label = "Optimize for unbalanced")]
GamRegression.cs (1)
67
[
TGUI
(Description = "Metric for pruning. (For regression, 1: L1, 2:L2; default L2")]
GamTrainer.cs (3)
57
[
TGUI
(SuggestedSweeps = "200,1500,9500")]
71
[
TGUI
(SuggestedSweeps = "0.001,0.1;log")]
109
[
TGUI
(SuggestedSweeps = "1,10,50")]
Microsoft.ML.ImageAnalytics (3)
ImageResizer.cs (3)
381
[
TGUI
(Label = "Isotropic with Padding")]
387
[
TGUI
(Label = "Isotropic with Cropping")]
393
[
TGUI
(Label = "Ignore aspect ratio and squeeze/stretch into target dimensions")]
Microsoft.ML.KMeansClustering (5)
KMeansPlusPlusTrainer.cs (5)
111
[
TGUI
(SuggestedSweeps = "5,10,20,40")]
126
[
TGUI
(Label = "Optimization Tolerance", Description = "Threshold for trainer convergence")]
133
[
TGUI
(Label = "Max Number of Iterations")]
141
[
TGUI
(Label = "Memory Budget (in MBs) for KMeans Acceleration")]
148
[
TGUI
(Label = "Number of threads")]
Microsoft.ML.LightGbm (10)
LightGbmArguments.cs (2)
150
[
TGUI
(Label = "Lambda(L2)", SuggestedSweeps = "0,0.5,1")]
164
[
TGUI
(Label = "Alpha(L1)", SuggestedSweeps = "0,0.5,1")]
LightGbmBinaryTrainer.cs (1)
159
[
TGUI
(Label = "Sigmoid", SuggestedSweeps = "0.5,1")]
LightGbmMulticlassTrainer.cs (1)
102
[
TGUI
(Label = "Sigmoid", SuggestedSweeps = "0.5,1")]
LightGbmRankingTrainer.cs (2)
132
[
TGUI
(Label = "Ranking Label Gain")]
139
[
TGUI
(Label = "Sigmoid", SuggestedSweeps = "0.5,1")]
LightGbmTrainerBase.cs (4)
83
[
TGUI
(Label = "Number of boosting iterations", SuggestedSweeps = "10,20,50,100,150,200")]
96
[
TGUI
(Label = "Learning Rate", SuggestedSweeps = "0.025-0.4;log")]
105
[
TGUI
(Description = "The maximum number of leaves per tree", SuggestedSweeps = "2-128;log;inc:4")]
114
[
TGUI
(Label = "Min Documents In Leaves", SuggestedSweeps = "1,10,20,50 ")]
Microsoft.ML.Mkl.Components (7)
OlsLinearRegression.cs (1)
83
[
TGUI
(SuggestedSweeps = "1e-6,0.1,1")]
SymSgdClassificationTrainer.cs (4)
96
[
TGUI
(SuggestedSweeps = "1,5,10,20,30,40,50")]
111
[
TGUI
(SuggestedSweeps = "<Auto>,1e1,1e0,1e-1,1e-2,1e-3")]
119
[
TGUI
(SuggestedSweeps = "0.0,1e-5,1e-5,1e-6,1e-7")]
129
[
TGUI
(SuggestedSweeps = "<Auto>,5,20")]
VectorWhitening.cs (2)
42
[
TGUI
(Label = "PCA whitening")]
46
[
TGUI
(Label = "ZCA whitening")]
Microsoft.ML.PCA (2)
PcaTrainer.cs (2)
97
[
TGUI
(SuggestedSweeps = "10,20,40,80")]
102
[
TGUI
(SuggestedSweeps = "10,20,40")]
Microsoft.ML.Recommender (7)
MatrixFactorizationTrainer.cs (7)
170
[
TGUI
(SuggestedSweeps = "0,12")]
183
[
TGUI
(SuggestedSweeps = "0.01,0.05,0.1,0.5,1")]
196
[
TGUI
(SuggestedSweeps = "8,16,64,128")]
204
[
TGUI
(SuggestedSweeps = "10,20,40")]
217
[
TGUI
(SuggestedSweeps = "0.001,0.01,0.1")]
237
[
TGUI
(SuggestedSweeps = "1,0.01,0.0001,0.000001")]
249
[
TGUI
(SuggestedSweeps = "0.000001,0,0001,0.01")]
Microsoft.ML.StandardTrainers (34)
LdSvm\LdSvmTrainer.cs (6)
79
[
TGUI
(SuggestedSweeps = "1,3,5,7")]
88
[
TGUI
(SuggestedSweeps = "0.1,0.01,0.001")]
97
[
TGUI
(SuggestedSweeps = "0.1,0.01,0.001")]
106
[
TGUI
(SuggestedSweeps = "0.1,0.01,0.001")]
115
[
TGUI
(SuggestedSweeps = "1.0,0.1,0.01")]
133
[
TGUI
(SuggestedSweeps = "10000,15000")]
Standard\LogisticRegression\LbfgsPredictorBase.cs (6)
46
[
TGUI
(Label = "L2 Weight", Description = "Weight of L2 regularizer term", SuggestedSweeps = "0,0.1,1")]
55
[
TGUI
(Label = "L1 Weight", Description = "Weight of L1 regularizer term", SuggestedSweeps = "0,0.1,1")]
65
[
TGUI
(Label = "Optimization Tolerance", Description = "Threshold for optimizer convergence", SuggestedSweeps = "1e-4,1e-7")]
74
[
TGUI
(Description = "Memory size for L-BFGS", SuggestedSweeps = "5,20,50")]
83
[
TGUI
(Label = "Max Number of Iterations")]
113
[
TGUI
(Label = "Initial Weights Scale", SuggestedSweeps = "0,0.1,0.5,1")]
Standard\MulticlassClassification\MetaMulticlassTrainer.cs (1)
24
[
TGUI
(Label = "Predictor Type", Description = "Type of underlying binary predictor")]
Standard\MulticlassClassification\OneVersusAllTrainer.cs (1)
106
[
TGUI
(Label = "Use Probability", Description = "Use probabilities (vs. raw outputs) to identify top-score category")]
Standard\Online\AveragedLinear.cs (3)
28
[
TGUI
(Label = "Learning rate", SuggestedSweeps = "0.01,0.1,0.5,1.0")]
41
[
TGUI
(Label = "Decrease Learning Rate", Description = "Decrease learning rate as iterations progress")]
70
[
TGUI
(Label = "L2 Regularization Weight")]
Standard\Online\LinearSvm.cs (2)
84
[
TGUI
(SuggestedSweeps = "0.00001-0.1;log;inc:10")]
90
[
TGUI
(Label = "Batch Size")]
Standard\Online\OnlineGradientDescent.cs (1)
73
[
TGUI
(Label = "Loss Function")]
Standard\Online\OnlineLinear.cs (3)
28
[
TGUI
(Label = "Number of Iterations", Description = "Number of training iterations through data", SuggestedSweeps = "1,10,100")]
37
[
TGUI
(NoSweep = true)]
48
[
TGUI
(Label = "Initial Weights Scale", SuggestedSweeps = "0,0.1,0.5,1")]
Standard\SdcaBinary.cs (11)
164
[
TGUI
(Label = "L2 Regularizer Constant", SuggestedSweeps = "<Auto>,1e-7,1e-6,1e-5,1e-4,1e-3,1e-2")]
175
[
TGUI
(Label = "L1 Soft Threshold", SuggestedSweeps = "<Auto>,0,0.25,0.5,0.75,1")]
187
[
TGUI
(Label = "Number of threads", SuggestedSweeps = "<Auto>,1,2,4")]
194
[
TGUI
(SuggestedSweeps = "0.001, 0.01, 0.1, 0.2")]
206
[
TGUI
(Label = "Max number of iterations", SuggestedSweeps = "<Auto>,10,20,100")]
235
[
TGUI
(SuggestedSweeps = "0, 0.01, 0.1, 1")]
1841
[
TGUI
(Label = "L2 Regularization Constant", SuggestedSweeps = "1e-7,5e-7,1e-6,5e-6,1e-5")]
1853
[
TGUI
(Label = "Number of threads", SuggestedSweeps = "1,2,4")]
1861
[
TGUI
(SuggestedSweeps = "1e-2,1e-3,1e-4,1e-5")]
1873
[
TGUI
(Label = "Max number of iterations", SuggestedSweeps = "1,5,10,20")]
1882
[
TGUI
(Label = "Initial Learning Rate (for SGD)")]
Microsoft.ML.Transforms (22)
OneHotEncoding.cs (4)
209
[
TGUI
(Label = "Output is a bag (multi-set) vector")]
215
[
TGUI
(Label = "Output is an indicator vector")]
221
[
TGUI
(Label = "Output is a key value")]
227
[
TGUI
(Label = "Output is binary encoded")]
Text\LdaTransform.cs (6)
60
[
TGUI
(SuggestedSweeps = "20,40,100,200")]
65
[
TGUI
(SuggestedSweeps = "1,10,100,200")]
70
[
TGUI
(SuggestedSweeps = "0.01,0.015,0.07,0.02")]
75
[
TGUI
(SuggestedSweeps = "2,4,8,16")]
80
[
TGUI
(SuggestedSweeps = "100,200,300,400")]
98
[
TGUI
(SuggestedSweeps = "10,20,30,40")]
Text\TextFeaturizingEstimator.cs (2)
203
[
TGUI
(Label = "Word Gram Extractor")]
244
[
TGUI
(Label = "Char Gram Extractor")]
Text\WordEmbeddingsExtractor.cs (10)
838
[
TGUI
(Label = "GloVe 50D")]
844
[
TGUI
(Label = "GloVe 100D")]
850
[
TGUI
(Label = "GloVe 200D")]
856
[
TGUI
(Label = "GloVe 300D")]
862
[
TGUI
(Label = "GloVe Twitter 25D")]
868
[
TGUI
(Label = "GloVe Twitter 50D")]
874
[
TGUI
(Label = "GloVe Twitter 100D")]
880
[
TGUI
(Label = "GloVe Twitter 200D")]
886
[
TGUI
(Label = "fastText Wikipedia 300D")]
892
[
TGUI
(Label = "Sentiment-Specific Word Embedding")]