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‎README.md‎

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@@ -36,7 +36,11 @@ Or alternatively you can add the Microsoft.ML package from within Visual Studio'
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To build ML.NET from source please visit our [developers guide](Documentation/project-docs/developer-guide.md).
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Live build status is coming soon.
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| | x64 Debug | x64 Release |
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|:---|----------------:|------------------:|
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|**Linux**|[![x64-debug](https://ci2.dot.net/job/dotnet_machinelearning/job/master/job/linux_debug/badge/icon)](https://ci2.dot.net/job/dotnet_machinelearning/job/master/job/linux_debug/lastCompletedBuild)|[![x64-release](https://ci2.dot.net/job/dotnet_machinelearning/job/master/job/linux_release/badge/icon)](https://ci2.dot.net/job/dotnet_machinelearning/job/master/job/linux_release/lastCompletedBuild)|
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|**macOS**|[![x64-debug](https://ci2.dot.net/job/dotnet_machinelearning/job/master/job/osx10.13_debug/badge/icon)](https://ci2.dot.net/job/dotnet_machinelearning/job/master/job/osx10.13_debug/lastCompletedBuild)|[![x64-release](https://ci2.dot.net/job/dotnet_machinelearning/job/master/job/osx10.13_release/badge/icon)](https://ci2.dot.net/job/dotnet_machinelearning/job/master/job/osx10.13_release/lastCompletedBuild)|
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|**Windows**|[![x64-debug](https://ci2.dot.net/job/dotnet_machinelearning/job/master/job/windows_nt_debug/badge/icon)](https://ci2.dot.net/job/dotnet_machinelearning/job/master/job/windows_nt_debug/lastCompletedBuild)|[![x64-release](https://ci2.dot.net/job/dotnet_machinelearning/job/master/job/windows_nt_release/badge/icon)](https://ci2.dot.net/job/dotnet_machinelearning/job/master/job/windows_nt_release/lastCompletedBuild)|
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## Contributing
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‎ROADMAP.md‎

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* Generative Additive Models
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* [SymSGD](https://arxiv.org/pdf/1705.08030.pdf) -a fast linear SGD learner
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* Factorization Machines
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* [ProtoNN and Bonsaii](https://www.microsoft.com/en-us/research/project/resource-efficient-ml-for-the-edge-and-endpoint-iot-devices/) for compact and effecient models
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* [ProtoNN and Bonsaii](https://www.microsoft.com/en-us/research/project/resource-efficient-ml-for-the-edge-and-endpoint-iot-devices/) for compact and efficient models
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* Integration with other ML packages
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* Accord.NET
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* etc.
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* Hybrid training of pipelines containing both DNN and non-DNN predictors
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* Additional ML tasks (*)
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* _Recommendation_ - Is a problem that can be phrased a: "For a given user, predict the ratings this user would give to the items that they have not explicitly rated yet"
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* _Anomaly Detection_, also known as _outlier detection_. It is a task to identify items, events or observations which do not conform to an expected pattern in the dataset. Typical examples are: detecting credit card fraud, medical problems or errors in text. Anomalies are also referred to as outliers,  novelties, noise, deviations and exceptions
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* _Anomaly Detection_, also known as _outlier detection_. It is a task to identify items, events or observations which do not conform to an expected pattern in the dataset. Typical examples are: detecting credit card fraud, medical problems or errors in text. Anomalies are also referred to as outliers, novelties, noise, deviations and exceptions
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* _Sequence Classification_ - learns from a series of examples in a sequence, and each item is assigned a distinct label, akin to a multiclass classification task
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* Additional Data source support
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* Data from SQL Databases, such as SQL Server

‎src/Microsoft.ML/Models/BinaryClassificationEvaluator.cs‎

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/// The trained PredictionModel to be evaluated.
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/// </param>
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/// <param name="testData">
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/// The test data that will be predicted and used to evaulate the model.
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/// The test data that will be predicted and used to evaluate the model.
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/// </param>
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/// <returns>
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/// A BinaryClassificationMetrics instance that describes how well the model performed against the test data.

‎src/Microsoft.ML/Models/ClassificationEvaluator.cs‎

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/// The trained multi-class classification PredictionModel to be evaluated.
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/// </param>
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/// <param name="testData">
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/// The test data that will be predicted and used to evaulate the model.
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/// The test data that will be predicted and used to evaluate the model.
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/// </param>
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/// <returns>
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/// A ClassificationMetrics instance that describes how well the model performed against the test data.

‎src/Microsoft.ML/Models/RegressionEvaluator.cs‎

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/// The trained PredictionModel to be evaluated.
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/// </param>
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/// <param name="testData">
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/// The test data that will be predicted and used to evaulate the model.
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/// The test data that will be predicted and used to evaluate the model.
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/// </param>
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/// <returns>
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/// A RegressionMetrics instance that describes how well the model performed against the test data.

‎src/Microsoft.ML/Runtime/EntryPoints/CodeGen/TransformGenerators.cs‎

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{
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writer.WriteLine("[Module(");
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_compName = prefix + component.LoadNames[0];
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var name = Name ?? PrettyPrintDiaplayName(component.LoadNames[0]);
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var name = Name ?? PrettyPrintDisplayName(component.LoadNames[0]);
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using (writer.Nest())
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{
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writer.WriteLine("Name = \"{0}\",", name);
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private void GenerateParameterAttribute(IndentingTextWriter w, string displayName, object defaultValue, string description,
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string parent = null, string parentType = null, string parentValue = null)
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{
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w.WriteLine("[Help(Display = @\"{0}\", ToolTip = \"{1}\")]", PrettyPrintDiaplayName(displayName), description);
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w.WriteLine("[Help(Display = @\"{0}\", ToolTip = \"{1}\")]", PrettyPrintDisplayName(displayName), description);
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if (parent != null)
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w.WriteLine("[Relevancy(Key = \"{0}\", Values = new object[] {{ {1}.{2} }})]", parent, parentType, parentValue);
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if (defaultValue != null)
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w.WriteLine("[Domain(DefaultValue = {0})]", defaultValue);
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w.WriteLine("[ModuleParameter]");
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}
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private string PrettyPrintDiaplayName(string displayName)
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private string PrettyPrintDisplayName(string displayName)
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{
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var sb = new StringBuilder();
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bool first = true;

‎src/Microsoft.ML/Runtime/EntryPoints/CrossValidationBinaryMacro.cs‎

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public sealed class Arguments
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// This is the data used in the cross validataion. It will be split into k folds
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// This is the data used in the cross validation. It will be split into k folds
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// and a model will be trained and evaluated for each fold.
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[TlcModule.OptionalInput]
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[Argument(ArgumentType.Required, HelpText = "The data set", SortOrder = 1)]

‎src/Microsoft.ML/Runtime/EntryPoints/CrossValidationMacro.cs‎

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public sealed class Arguments
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// This is the data used in the cross validation. It will be split into k folds
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// and a model will be trained and evaluated for each fold.
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[TlcModule.OptionalInput]
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[Argument(ArgumentType.Required, HelpText = "The data set", SortOrder = 1)]

‎test/data/README.md‎

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MICROSOFT PROVIDES THE DATASETS ON AN "AS IS" BASIS. MICROSOFT MAKES NO WARRANTIES, EXPRESS OR IMPLIED, GUARANTEES OR CONDITIONS WITH RESPECT TO YOUR USE OF THE DATASETS. TO THE EXTENT PERMITTED UNDER YOUR LOCAL LAW, MICROSOFT DISCLAIMS ALL LIABILITY FOR ANY DAMAGES OR LOSSES, INLCUDING DIRECT, CONSEQUENTIAL, SPECIAL, INDIRECT, INCIDENTAL OR PUNITIVE, RESULTING FROM YOUR USE OF THE DATASETS.
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### Wikipedia Detox
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Redistributing the dataset "wikipedia-detox-250-line-data.tsv" with attribution:
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The datasets are provided under the original terms that Microsoft received such datasets. See below for more information about each dataset.
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> Wulczyn, Ellery; Thain, Nithum; Dixon, Lucas (2016): Wikipedia Detox. figshare. https://doi.org/10.6084/m9.figshare.4054689
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### Wikipedia Detox
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With modifications by taking a sample of rows and reducing rough language.
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>This dataset is provided under [CC0](https://creativecommons.org/share-your-work/public-domain/cc0/). Redistributing the dataset "wikipedia-detox-250-line-data.tsv" with attribution:
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>
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> Wulczyn, Ellery; Thain, Nithum; Dixon, Lucas (2016): Wikipedia Detox. figshare.
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>
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>With modifications by taking a sample of rows and reducing rough language.
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>
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>Original source: https://doi.org/10.6084/m9.figshare.4054689
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>
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>Original readme: https://meta.wikimedia.org/wiki/Research:Detox
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### UCI Adult Dataset
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