FFM XML Doc And Add One Missing Sample File - #3374
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## master #3374 +/- ##
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+ Coverage 72.7% 72.77% +0.07%
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Files 807 808 +1
Lines 145172 145452 +280
Branches 16225 16244 +19
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+ Hits 105541 105849 +308
+ Misses 35217 35183 -34
- Partials 4414 4420 +6
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shauheen
reviewed
Apr 18, 2019
| /// or [FieldAwareFactorizationMachine(Options)](xref:Microsoft.ML.FactorizationMachineExtensions.FieldAwareFactorizationMachine(Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers,Microsoft.ML.Trainers.FieldAwareFactorizationMachineTrainer.Options)). | ||
| /// | ||
| /// In contrast to other binary classifiers which can only support one feature column, field-aware factorization machine can consume multiple feature columns. | ||
| /// Each column is viewed a container of some fatures and such a container is called a field. |
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a container [](start = 29, length = 12)
as #Resolved
shauheen
reviewed
Apr 18, 2019
| /// or [FieldAwareFactorizationMachine(Options)](xref:Microsoft.ML.FactorizationMachineExtensions.FieldAwareFactorizationMachine(Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers,Microsoft.ML.Trainers.FieldAwareFactorizationMachineTrainer.Options)). | ||
| /// | ||
| /// In contrast to other binary classifiers which can only support one feature column, field-aware factorization machine can consume multiple feature columns. | ||
| /// Each column is viewed a container of some fatures and such a container is called a field. |
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fatures [](start = 50, length = 7)
features #Resolved
shauheen
reviewed
Apr 18, 2019
| /// | ||
| /// In contrast to other binary classifiers which can only support one feature column, field-aware factorization machine can consume multiple feature columns. | ||
| /// Each column is viewed a container of some fatures and such a container is called a field. | ||
| /// The motivation of splitting features into different fields is to model features from different distributions in a different way. |
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in a different way [](start = 117, length = 18)
independently #Resolved
shauheen
reviewed
Apr 18, 2019
| /// ### Background | ||
| /// Factorization machine family is a powerful model group for supervised learning problems. | ||
| /// It was first introduced in Steffen Rendle's [Factorization Machines](http://ieeexplore.ieee.org/document/5694074/?reload=true) paper in 2010. | ||
| /// Later, one of its generalized version, field-aware factorization machine, became an important predictive module in recent recommender systems and click-through rate prediction contests. |
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version [](start = 38, length = 7)
versions #Resolved
shauheen
reviewed
Apr 18, 2019
| /// Factorization machine family is a powerful model group for supervised learning problems. | ||
| /// It was first introduced in Steffen Rendle's [Factorization Machines](http://ieeexplore.ieee.org/document/5694074/?reload=true) paper in 2010. | ||
| /// Later, one of its generalized version, field-aware factorization machine, became an important predictive module in recent recommender systems and click-through rate prediction contests. | ||
| /// For examples, see winning solutions in Steffen Rendle's KDD-Cup 2012 ([Track 1](http://www.kdd.org/kdd-cup/view/kdd-cup-2012-track-1) and[Track 2](http://www.kdd.org/kdd-cup/view/kdd-cup-2012-track-2)), |
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nd[T [](start = 143, length = 4)
space #Resolved
shauheen
reviewed
Apr 18, 2019
| /// The corresponding score is $\hat{y}\left(\boldsymbol{x}\right) = \left\langle \boldsymbol{w}, \boldsymbol{x} \right\rangle + \sum_{j = 1}^n \sum_{j' = j + 1}^n \left\langle \boldsymbol{v}_{j, {\mathcal F}(j')} , \boldsymbol{v}_{j', {\mathcal F}(j)} \right\rangle x_j x_{j'}$, | ||
| /// where $\left\langle \cdot, \cdot \right\rangle$ is the inner product operator, $\boldsymbol{w}\in{\mathbb R}^n$ stores the linear coefficients, and $\boldsymbol{v}_{j, f}\in {\mathbb R}^k$ is the $j$-th feature's representation in the $f$-th field's latent space. | ||
| /// Note that $k$ is the latent dimension specified by the user. | ||
| /// The predicted label is the sign of $\hat{y}$. If $\hat{y} > 0$, this model predicts true and false otherwise. |
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nd false otherwise [](start = 98, length = 18)
otherwise it predicts false. #Resolved
codemzs
reviewed
Apr 19, 2019
| { | ||
| /// <summary> | ||
| /// Predict a target using a field-aware factorization machine algorithm. | ||
| /// Create an <see cref="FieldAwareFactorizationMachineTrainer"/>, which predicts a target using a field-aware factorization machine trained over boolean label data. |
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an [](start = 19, length = 2)
Check with @natke if we need "an" for my PRs there was no "an" or "a" #Resolved
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codemzs
reviewed
Apr 19, 2019
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| /// <summary> | ||
| /// Predict a target using a field-aware factorization machine algorithm. | ||
| /// Create an <see cref="FieldAwareFactorizationMachineTrainer"/>, which predicts a target using a field-aware factorization machine trained over boolean label data. |
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Create an [](start = 11, length = 62)
Create using advanced options, ... #Resolved
wschin
commented
Apr 20, 2019
…zationMachineCatalog.cs
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Toward #2522 following #3218.