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Merge pull request #1387 from RealKai42/kaiyi/feat/deprecate-some-pipeline-buildin-component
docs: add deprecated info to PCA-Based Anomaly Detection, Poisson Re…
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articles/machine-learning/component-reference/feature-hashing.md

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title: "Feature Hashing component reference"
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titleSuffix: Azure Machine Learning
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description: Learn how to use the Feature Hashing component in the Azure Machine Learning designer to featurize text data.
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description: "[Deprecated] Learn how to use the Feature Hashing component in the Azure Machine Learning designer to featurize text data."
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services: machine-learning
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ms.service: azure-machine-learning
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ms.subservice: core
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# Feature Hashing component reference
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> [!WARNING]
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> This component HAS BEEN DEPRECATED because its dependency, the [NimbusML](https://github.com/microsoft/NimbusML) project, is no longer actively maintained. As a result, this component will not receive future updates or security patches.
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> We plan to remove this component in upcoming releases. Users are recommended to migrate to alternative solutions to ensure continued support and security.
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This article describes a component included in Azure Machine Learning designer.
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Use the Feature Hashing component to transform a stream of English text into a set of integer features. You can then pass this hashed feature set to a machine learning algorithm to train a text analytics model.

articles/machine-learning/component-reference/pca-based-anomaly-detection.md

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title: "PCA-Based Anomaly Detection: Component reference"
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titleSuffix: Azure Machine Learning
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description: Learn how to use the PCA-Based Anomaly Detection component to create an anomaly detection model based on principal component analysis (PCA).
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description: "[Deprecated] Learn how to use the PCA-Based Anomaly Detection component to create an anomaly detection model based on principal component analysis (PCA)."
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services: machine-learning
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ms.service: azure-machine-learning
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ms.subservice: core
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# PCA-Based Anomaly Detection component
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> [!WARNING]
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> This component HAS BEEN DEPRECATED because its dependency, the [NimbusML](https://github.com/microsoft/NimbusML) project, is no longer actively maintained. As a result, this component will not receive future updates or security patches.
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> We plan to remove this component in upcoming releases. Users are recommended to migrate to alternative solutions to ensure continued support and security.
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This article describes how to use the PCA-Based Anomaly Detection component in Azure Machine Learning designer, to create an anomaly detection model based on principal component analysis (PCA).
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This component helps you build a model in scenarios where it's easy to get training data from one class, such as valid transactions, but difficult to get sufficient samples of the targeted anomalies.

articles/machine-learning/component-reference/poisson-regression.md

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title: "Poisson Regression: Component reference"
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titleSuffix: Azure Machine Learning
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description: Learn how to use the Poisson Regression component in Azure Machine Learning designer to create a Poisson regression model.
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description: "[Deprecated] Learn how to use the Poisson Regression component in Azure Machine Learning designer to create a Poisson regression model."
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services: machine-learning
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ms.service: azure-machine-learning
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ms.subservice: core
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# Poisson Regression
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> [!WARNING]
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> This component HAS BEEN DEPRECATED because its dependency, the [NimbusML](https://github.com/microsoft/NimbusML) project, is no longer actively maintained. As a result, this component will not receive future updates or security patches.
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> We plan to remove this component in upcoming releases. Users are recommended to migrate to alternative solutions to ensure continued support and security.
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This article describes a component in Azure Machine Learning designer.
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Use this component to create a Poisson regression model in a pipeline. Poisson regression is intended for predicting numeric values, typically counts. Therefore, you should use this component to create your regression model only if the values you are trying to predict fit the following conditions:

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