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MutualInformationFeatureSelectingEstimator.xml
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MutualInformationFeatureSelectingEstimator.xml
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<Type Name="MutualInformationFeatureSelectingEstimator" FullName="Microsoft.ML.Transforms.MutualInformationFeatureSelectingEstimator">
<TypeSignature Language="C#" Value="public sealed class MutualInformationFeatureSelectingEstimator : Microsoft.ML.IEstimator<Microsoft.ML.ITransformer>" />
<TypeSignature Language="ILAsm" Value=".class public auto ansi sealed beforefieldinit MutualInformationFeatureSelectingEstimator extends System.Object implements class Microsoft.ML.IEstimator`1<class Microsoft.ML.ITransformer>" />
<TypeSignature Language="DocId" Value="T:Microsoft.ML.Transforms.MutualInformationFeatureSelectingEstimator" />
<TypeSignature Language="VB.NET" Value="Public NotInheritable Class MutualInformationFeatureSelectingEstimator
Implements IEstimator(Of ITransformer)" />
<TypeSignature Language="F#" Value="type MutualInformationFeatureSelectingEstimator = class
 interface IEstimator<ITransformer>" />
<AssemblyInfo>
<AssemblyName>Microsoft.ML.Transforms</AssemblyName>
<AssemblyVersion>1.0.0.0</AssemblyVersion>
</AssemblyInfo>
<Base>
<BaseTypeName>System.Object</BaseTypeName>
</Base>
<Interfaces>
<Interface>
<InterfaceName>Microsoft.ML.IEstimator<Microsoft.ML.ITransformer></InterfaceName>
</Interface>
</Interfaces>
<Docs>
<summary>
Selects the top k slots across all specified columns ordered by their mutual information with the label column
(what you can learn about the label by observing the value of the specified column).
</summary>
<remarks>
<format type="text/markdown">< or [key](xref:Microsoft.ML.Data.KeyDataViewType) data types|
| Output column data type | Same as the input column|
| Exportable to ONNX | Yes |
Formally, the mutual information can be written as:
$\text{MI}(X,Y) = E_{x,y}[\log(P(x,y)) - \log(P(x)) - \log(P(y))]$ where $x$ and $y$ are observations of random variables $X$ and $Y$.
where the expectation E is taken over the joint distribution of X and Y.
Here P(x, y) is the joint probability density function of X and Y, P(x) and P(y) are the marginal probability density functions of X and Y respectively.
In general, a higher mutual information between the dependent variable(or label) and an independent variable(or feature) means
that the label has higher mutual dependence over that feature.
It keeps the top slots in output features with the largest mutual information with the label.
For example, for the following Features and Label column, if we specify that we want the top 2 slots(vector elements) that have the higher correlation
with the label column, the output of applying this Estimator would keep the first and the third slots only, because their values
are more correlated with the values in the Label column.
| Label | Features |
| -- | -- |
|True |4,6,0 |
|False|0,7,5 |
|True |4,7,0 |
|False|0,7,0 |
This is how the dataset above would look, after fitting the estimator, and transforming the data with the resulting transformer:
| Label | Features |
| -- | -- |
|True |4,0 |
|False|0,5 |
|True |4,0 |
|False|0,0 |
Check the See Also section for links to usage examples.
]]></format>
</remarks>
<altmember cref="M:Microsoft.ML.FeatureSelectionCatalog.SelectFeaturesBasedOnMutualInformation(Microsoft.ML.TransformsCatalog.FeatureSelectionTransforms,System.String,System.String,System.String,System.Int32,System.Int32)" />
<altmember cref="M:Microsoft.ML.FeatureSelectionCatalog.SelectFeaturesBasedOnMutualInformation(Microsoft.ML.TransformsCatalog.FeatureSelectionTransforms,Microsoft.ML.InputOutputColumnPair[],System.String,System.Int32,System.Int32)" />
</Docs>
<Members>
<Member MemberName="Fit">
<MemberSignature Language="C#" Value="public Microsoft.ML.ITransformer Fit (Microsoft.ML.IDataView input);" />
<MemberSignature Language="ILAsm" Value=".method public hidebysig newslot virtual instance class Microsoft.ML.ITransformer Fit(class Microsoft.ML.IDataView input) cil managed" />
<MemberSignature Language="DocId" Value="M:Microsoft.ML.Transforms.MutualInformationFeatureSelectingEstimator.Fit(Microsoft.ML.IDataView)" />
<MemberSignature Language="VB.NET" Value="Public Function Fit (input As IDataView) As ITransformer" />
<MemberSignature Language="F#" Value="abstract member Fit : Microsoft.ML.IDataView -> Microsoft.ML.ITransformer
override this.Fit : Microsoft.ML.IDataView -> Microsoft.ML.ITransformer" Usage="mutualInformationFeatureSelectingEstimator.Fit input" />
<MemberType>Method</MemberType>
<Implements>
<InterfaceMember>M:Microsoft.ML.IEstimator`1.Fit(Microsoft.ML.IDataView)</InterfaceMember>
</Implements>
<AssemblyInfo>
<AssemblyName>Microsoft.ML.Transforms</AssemblyName>
<AssemblyVersion>1.0.0.0</AssemblyVersion>
</AssemblyInfo>
<ReturnValue>
<ReturnType>Microsoft.ML.ITransformer</ReturnType>
</ReturnValue>
<Parameters>
<Parameter Name="input" Type="Microsoft.ML.IDataView" />
</Parameters>
<Docs>
<param name="input">To be added.</param>
<summary>
Trains and returns a <see cref="T:Microsoft.ML.ITransformer" />.
</summary>
<returns>To be added.</returns>
<remarks>To be added.</remarks>
</Docs>
</Member>
<Member MemberName="GetOutputSchema">
<MemberSignature Language="C#" Value="public Microsoft.ML.SchemaShape GetOutputSchema (Microsoft.ML.SchemaShape inputSchema);" />
<MemberSignature Language="ILAsm" Value=".method public hidebysig newslot virtual instance class Microsoft.ML.SchemaShape GetOutputSchema(class Microsoft.ML.SchemaShape inputSchema) cil managed" />
<MemberSignature Language="DocId" Value="M:Microsoft.ML.Transforms.MutualInformationFeatureSelectingEstimator.GetOutputSchema(Microsoft.ML.SchemaShape)" />
<MemberSignature Language="VB.NET" Value="Public Function GetOutputSchema (inputSchema As SchemaShape) As SchemaShape" />
<MemberSignature Language="F#" Value="abstract member GetOutputSchema : Microsoft.ML.SchemaShape -> Microsoft.ML.SchemaShape
override this.GetOutputSchema : Microsoft.ML.SchemaShape -> Microsoft.ML.SchemaShape" Usage="mutualInformationFeatureSelectingEstimator.GetOutputSchema inputSchema" />
<MemberType>Method</MemberType>
<Implements>
<InterfaceMember>M:Microsoft.ML.IEstimator`1.GetOutputSchema(Microsoft.ML.SchemaShape)</InterfaceMember>
</Implements>
<AssemblyInfo>
<AssemblyName>Microsoft.ML.Transforms</AssemblyName>
<AssemblyVersion>1.0.0.0</AssemblyVersion>
</AssemblyInfo>
<ReturnValue>
<ReturnType>Microsoft.ML.SchemaShape</ReturnType>
</ReturnValue>
<Parameters>
<Parameter Name="inputSchema" Type="Microsoft.ML.SchemaShape" />
</Parameters>
<Docs>
<param name="inputSchema">To be added.</param>
<summary>
Returns the <see cref="T:Microsoft.ML.SchemaShape" /> of the schema which will be produced by the transformer.
Used for schema propagation and verification in a pipeline.
</summary>
<returns>To be added.</returns>
<remarks>To be added.</remarks>
</Docs>
</Member>
</Members>
</Type>