API reference - Fix placeholder links (#3359)

* Removed tmpurl_lr

* Replaced tmpurl_regularization

* Replaced tmpurl_loss

* Replaced tmpurl_calib

Author: Shahab Moradi

src/Microsoft.ML.StandardTrainers/Standard/Online/AveragedLinear.cs

@@ -21,7 +21,7 @@ namespace Microsoft.ML.Trainers
     public abstract class AveragedLinearOptions : OnlineLinearOptions
     {
         /// <summary>
-        /// <a href="tmpurl_lr">Learning rate</a>.
+        /// Learning rate.
         /// </summary>
         [Argument(ArgumentType.AtMostOnce, HelpText = "Learning rate", ShortName = "lr", SortOrder = 50)]
         [TGUI(Label = "Learning rate", SuggestedSweeps = "0.01,0.1,0.5,1.0")]
@@ -61,7 +61,7 @@ public abstract class AveragedLinearOptions : OnlineLinearOptions
         public bool LazyUpdate = true;
 
         /// <summary>
-        /// The L2 weight for <a href='tmpurl_regularization'>regularization</a>.
+        /// The L2 weight for <a href='https://en.wikipedia.org/wiki/Regularization_(mathematics)'>regularization</a>.
         /// </summary>
         [Argument(ArgumentType.AtMostOnce, HelpText = "L2 Regularization Weight", ShortName = "reg,L2RegularizerWeight", SortOrder = 50)]
         [TGUI(Label = "L2 Regularization Weight")]

src/Microsoft.ML.StandardTrainers/Standard/Online/AveragedPerceptron.cs

@@ -79,18 +79,18 @@ public sealed class AveragedPerceptronTrainer : AveragedLinearTrainer<BinaryPred
         public sealed class Options : AveragedLinearOptions
         {
             /// <summary>
-            /// A custom <a href="tmpurl_loss">loss</a>.
+            /// A custom <a href="https://en.wikipedia.org/wiki/Loss_function">loss</a>.
             /// </summary>
             [Argument(ArgumentType.Multiple, Name = "LossFunction", HelpText = "Loss Function", ShortName = "loss", SortOrder = 50)]
             internal ISupportClassificationLossFactory ClassificationLossFunctionFactory = new HingeLoss.Options();
 
             /// <summary>
-            /// A custom <a href="tmpurl_loss">loss</a>.
+            /// A custom <a href="https://en.wikipedia.org/wiki/Loss_function">loss</a>.
             /// </summary>
             public IClassificationLoss LossFunction { get; set; }
 
             /// <summary>
-            /// The <a href="tmpurl_calib">calibrator</a> for producing probabilities. Default is exponential (aka Platt) calibration.
+            /// The <a href="https://en.wikipedia.org/wiki/Calibration_(statistics)">calibrator</a> for producing probabilities. Default is exponential (aka Platt) calibration.
             /// </summary>
             [Argument(ArgumentType.AtMostOnce, HelpText = "The calibrator kind to apply to the predictor. Specify null for no calibration", Visibility = ArgumentAttribute.VisibilityType.EntryPointsOnly)]
             internal ICalibratorTrainerFactory Calibrator = new PlattCalibratorTrainerFactory();

src/Microsoft.ML.StandardTrainers/Standard/Online/OnlineGradientDescent.cs

@@ -52,7 +52,7 @@ public sealed class Options : AveragedLinearOptions
             internal ISupportRegressionLossFactory RegressionLossFunctionFactory = new SquaredLossFactory();
 
             /// <summary>
-            /// A custom <a href="tmpurl_loss">loss</a>.
+            /// A custom <a href="https://en.wikipedia.org/wiki/Loss_function">loss</a>.
             /// </summary>
             public IRegressionLoss LossFunction { get; set; }
 

src/Microsoft.ML.StandardTrainers/Standard/SdcaBinary.cs

@@ -157,7 +157,7 @@ public abstract class SdcaTrainerBase<TOptions, TTransformer, TModel> : Stochast
         public abstract class OptionsBase : TrainerInputBaseWithWeight
         {
             /// <summary>
-            /// The L2 <a href='tmpurl_regularization'>regularization</a> hyperparameter.
+            /// The L2 <a href='https://en.wikipedia.org/wiki/Regularization_(mathematics)'>regularization</a> hyperparameter.
             /// </summary>
             [Argument(ArgumentType.AtMostOnce, HelpText = "L2 regularizer constant. By default the l2 constant is automatically inferred based on data set.", NullName = "<Auto>", ShortName = "l2, L2Const", SortOrder = 1)]
             [TGUI(Label = "L2 Regularizer Constant", SuggestedSweeps = "<Auto>,1e-7,1e-6,1e-5,1e-4,1e-3,1e-2")]
@@ -166,7 +166,7 @@ public abstract class OptionsBase : TrainerInputBaseWithWeight
 
             // REVIEW: make the default positive when we know how to consume a sparse model
             /// <summary>
-            /// The L1 <a href='tmpurl_regularization'>regularization</a> hyperparameter.
+            /// The L1 <a href='https://en.wikipedia.org/wiki/Regularization_(mathematics)'>regularization</a> hyperparameter.
             /// </summary>
             [Argument(ArgumentType.AtMostOnce, HelpText = "L1 soft threshold (L1/L2). Note that it is easier to control and sweep using the threshold parameter than the raw L1-regularizer constant. By default the l1 threshold is automatically inferred based on data set.",
                 NullName = "<Auto>", Name = "L1Threshold", ShortName = "l1", SortOrder = 2)]
@@ -1547,7 +1547,7 @@ private protected override BinaryPredictionTransformer<TModelParameters> MakeTra
 
     /// <summary>
     /// The <see cref="IEstimator{TTransformer}"/> for training a binary logistic regression classification model using the stochastic dual coordinate ascent method.
-    /// The trained model is <a href='tmpurl_calib'>calibrated</a> and can produce probability by feeding the output value of the
+    /// The trained model is <a href='https://en.wikipedia.org/wiki/Calibration_(statistics)'>calibrated</a> and can produce probability by feeding the output value of the
     /// linear function to a <see cref="PlattCalibrator"/>.
     /// </summary>
     /// <include file='doc.xml' path='doc/members/member[@name="SDCA_remarks"]/*' />
@@ -1623,7 +1623,7 @@ public sealed class SdcaNonCalibratedBinaryTrainer : SdcaBinaryTrainerBase<Linea
         public sealed class Options : BinaryOptionsBase
         {
             /// <summary>
-            /// The custom <a href="tmpurl_loss">loss</a>.
+            /// The custom <a href="https://en.wikipedia.org/wiki/Loss_function">loss</a>.
             /// </summary>
             /// <value>
             /// If unspecified, <see cref="LogLoss"/> will be used.
@@ -1632,7 +1632,7 @@ public sealed class Options : BinaryOptionsBase
             internal ISupportSdcaClassificationLossFactory LossFunctionFactory = new LogLossFactory();
 
             /// <summary>
-            /// The custom <a href="tmpurl_loss">loss</a>.
+            /// The custom <a href="https://en.wikipedia.org/wiki/Loss_function">loss</a>.
             /// </summary>
             /// <value>
             /// If unspecified, <see cref="LogLoss"/> will be used.
@@ -1776,7 +1776,7 @@ public abstract class SgdBinaryTrainerBase<TModel> :
         public class OptionsBase : TrainerInputBaseWithWeight
         {
             /// <summary>
-            /// The L2 weight for <a href='tmpurl_regularization'>regularization</a>.
+            /// The L2 weight for <a href='https://en.wikipedia.org/wiki/Regularization_(mathematics)'>regularization</a>.
             /// </summary>
             [Argument(ArgumentType.AtMostOnce, HelpText = "L2 Regularization constant", ShortName = "l2, L2Weight", SortOrder = 50)]
             [TGUI(Label = "L2 Regularization Constant", SuggestedSweeps = "1e-7,5e-7,1e-6,5e-6,1e-5")]
@@ -1814,7 +1814,7 @@ public class OptionsBase : TrainerInputBaseWithWeight
             public int NumberOfIterations = Defaults.NumberOfIterations;
 
             /// <summary>
-            /// The initial <a href="tmpurl_lr">learning rate</a> used by SGD.
+            /// The initial learning rate used by SGD.
             /// </summary>
             [Argument(ArgumentType.AtMostOnce, HelpText = "Initial learning rate (only used by SGD)", Name = "InitialLearningRate", ShortName = "ilr,lr,InitLearningRate")]
             [TGUI(Label = "Initial Learning Rate (for SGD)")]
@@ -2171,7 +2171,7 @@ private protected override void CheckLabel(RoleMappedData examples, out int weig
 
     /// <summary>
     /// The <see cref="IEstimator{TTransformer}"/> for training logistic regression using a parallel stochastic gradient method.
-    /// The trained model is <a href='tmpurl_calib'>calibrated</a> and can produce probability by feeding the output value of the
+    /// The trained model is <a href='https://en.wikipedia.org/wiki/Calibration_(statistics)'>calibrated</a> and can produce probability by feeding the output value of the
     /// linear function to a <see cref="PlattCalibrator"/>.
     /// </summary>
     /// <remarks>

src/Microsoft.ML.StandardTrainers/Standard/SdcaMulticlass.cs

@@ -42,7 +42,7 @@ public abstract class SdcaMulticlassTrainerBase<TModel> : SdcaTrainerBase<SdcaMu
         public class MulticlassOptions : OptionsBase
         {
             /// <summary>
-            /// The custom <a href="tmpurl_loss">loss</a>.
+            /// The custom <a href="https://en.wikipedia.org/wiki/Loss_function">loss</a>.
             /// </summary>
             /// <value>
             /// If unspecified, <see cref="LogLoss"/> will be used.

src/Microsoft.ML.StandardTrainers/Standard/SdcaRegression.cs

@@ -38,7 +38,7 @@ public sealed class SdcaRegressionTrainer : SdcaTrainerBase<SdcaRegressionTraine
         public sealed class Options : OptionsBase
         {
             /// <summary>
-            /// A custom <a href="tmpurl_loss">loss</a>.
+            /// A custom <a href="https://en.wikipedia.org/wiki/Loss_function">loss</a>.
             /// </summary>
             /// <value>
             /// Defaults to <see cref="SquaredLoss"/>
@@ -47,7 +47,7 @@ public sealed class Options : OptionsBase
             internal ISupportSdcaRegressionLossFactory LossFunctionFactory = new SquaredLossFactory();
 
             /// <summary>
-            /// A custom <a href="tmpurl_loss">loss</a>.
+            /// A custom <a href="https://en.wikipedia.org/wiki/Loss_function">loss</a>.
             /// </summary>
             /// <value>
             /// Defaults to <see cref="SquaredLoss"/>