96 writes to Label
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 (5)
FastTreeArguments.cs (4)
70
[TGUI(
Label
= "Optimize for unbalanced")]
695
[TGUI(
Label
= "Early Stopping Rule", Description = "Early stopping rule. (Validation set (/valid) is required.)")]
755
[TGUI(
Label
= "Learning Rate", SuggestedSweeps = "0.025-0.4;log")]
763
[TGUI(
Label
= "Shrinkage", SuggestedSweeps = "0.25-4;log")]
GamClassification.cs (1)
70
[TGUI(
Label
= "Optimize for unbalanced")]
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 (4)
KMeansPlusPlusTrainer.cs (4)
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 (9)
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 (3)
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")]
114
[TGUI(
Label
= "Min Documents In Leaves", SuggestedSweeps = "1,10,20,50 ")]
Microsoft.ML.Mkl.Components (2)
VectorWhitening.cs (2)
42
[TGUI(
Label
= "PCA whitening")]
46
[TGUI(
Label
= "ZCA whitening")]
Microsoft.ML.StandardTrainers (22)
Standard\LogisticRegression\LbfgsPredictorBase.cs (5)
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")]
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 (1)
90
[TGUI(
Label
= "Batch Size")]
Standard\Online\OnlineGradientDescent.cs (1)
73
[TGUI(
Label
= "Loss Function")]
Standard\Online\OnlineLinear.cs (2)
28
[TGUI(
Label
= "Number of Iterations", Description = "Number of training iterations through data", SuggestedSweeps = "1,10,100")]
48
[TGUI(
Label
= "Initial Weights Scale", SuggestedSweeps = "0,0.1,0.5,1")]
Standard\SdcaBinary.cs (8)
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")]
206
[TGUI(
Label
= "Max number of iterations", SuggestedSweeps = "<Auto>,10,20,100")]
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")]
1873
[TGUI(
Label
= "Max number of iterations", SuggestedSweeps = "1,5,10,20")]
1882
[TGUI(
Label
= "Initial Learning Rate (for SGD)")]
Microsoft.ML.Transforms (16)
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\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")]