Feature Exploration/Learning - Revision history

BPeat at 20:56, 28 April 2024

2024-04-28T20:56:25Z

BPeat at 01:46, 27 April 2024

2024-04-27T01:46:53Z

BPeat at 01:11, 19 September 2023

2023-09-19T01:11:10Z

BPeat: /* Feature Store */

2023-09-19T01:09:03Z

‎Feature Store

BPeat: /* Feature Store */

2023-09-19T01:01:47Z

‎Feature Store

BPeat at 00:47, 19 September 2023

2023-09-19T00:47:49Z

BPeat at 00:46, 19 September 2023

2023-09-19T00:46:26Z

BPeat at 00:05, 9 September 2023

2023-09-09T00:05:32Z

BPeat at 11:39, 17 August 2023

2023-08-17T11:39:55Z

BPeat at 00:39, 10 July 2023

2023-07-10T00:39:35Z

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 ||
 <youtube>_XOKz5VlTQY</youtube>
-<b>Recent Advances in Feature Selection: A Data Perspective part 1
+<b>Recent Advances in Feature Selection: A Data [[Perspective]] part 1
-</b><br>Authors: Huan Liu, Department of Computer Science and Engineering, Arizona State University   Jundong Li, School of Computing, Informatics and Decision Systems Engineering, Arizona State University   Jiliang Tang, Department of Computer Science and Engineering, Michigan State University   Feature selection, as a data preprocessing strategy, is imperative in preparing high-dimensional data for myriad of data mining and machine learning tasks. By selecting a subset of features of high quality, feature selection can help build simpler and more comprehensive models, improve data mining performance, and prepare clean and understandable data. The proliferation of big data in recent years has presented substantial challenges and opportunities for feature selection research. In this tutorial, we provide a comprehensive overview of recent advances in feature selection research from a data perspective. After we introduce some basic concepts, we review state-of-the-art feature selection algorithms and recent techniques of feature selection for structured, social, heterogeneous, and streaming data. In particular, we also discuss what the role of feature selection is in the [[context]] of deep learning and how feature selection is related to feature engineering. To facilitate and promote the research in this community, we present an open-source feature selection repository scikit-feature that consists of most of the popular feature selection algorithms. We conclude our discussion with some open problems and pressing issues in future research.
+</b><br>Authors: Huan Liu, Department of Computer Science and Engineering, Arizona State University   Jundong Li, School of Computing, Informatics and Decision Systems Engineering, Arizona State University   Jiliang Tang, Department of Computer Science and Engineering, Michigan State University   Feature selection, as a data preprocessing strategy, is imperative in preparing high-dimensional data for myriad of data mining and machine learning tasks. By selecting a subset of features of high quality, feature selection can help build simpler and more comprehensive models, improve data mining performance, and prepare clean and understandable data. The proliferation of big data in recent years has presented substantial challenges and opportunities for feature selection research. In this tutorial, we provide a comprehensive overview of recent advances in feature selection research from a data [[perspective]]. After we introduce some basic concepts, we review state-of-the-art feature selection algorithms and recent techniques of feature selection for structured, social, heterogeneous, and streaming data. In particular, we also discuss what the role of feature selection is in the [[context]] of deep learning and how feature selection is related to feature engineering. To facilitate and promote the research in this community, we present an open-source feature selection repository scikit-feature that consists of most of the popular feature selection algorithms. We conclude our discussion with some open problems and pressing issues in future research.
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 * [[Excel]] ... [[LangChain#Documents|Documents]] ... [[Database|Database; Vector & Relational]] ... [[Graph]] ... [[LlamaIndex]]
 * [[Analytics]] ... [[Visualization]] ... [[Graphical Tools for Modeling AI Components|Graphical Tools]] ... [[Diagrams for Business Analysis|Diagrams]] & [[Generative AI for Business Analysis|Business Analysis]] ... [[Requirements Management|Requirements]] ... [[Loop]] ... [[Bayes]] ... [[Network Pattern]]
-* [[Development]] ... [[Notebooks]] ... [[Development#AI Pair Programming Tools|AI Pair Programming]] ... [[Codeless Options, Code Generators, Drag n' Drop|Codeless, Generators, Drag n' Drop]] ... [[Algorithm Administration#AIOps/MLOps|AIOps/MLOps]] ... [[Platforms: AI/Machine Learning as a Service (AIaaS/MLaaS)|AIaaS/MLaaS]]
+* [[Development]] ... [[Notebooks]] ... [[Development#AI Pair Programming Tools|AI Pair Programming]] ... [[Codeless Options, Code Generators, Drag n' Drop|Codeless]] ... [[Hugging Face]] ... [[Algorithm Administration#AIOps/MLOps|AIOps/MLOps]] ... [[Platforms: AI/Machine Learning as a Service (AIaaS/MLaaS)|AIaaS/MLaaS]]
 * [https://en.wikipedia.org/wiki/Feature_selection Feature selection | Wikipedia]
 * [https://www.kdnuggets.com/2018/10/notes-feature-preprocessing-what-why-how.html Notes on Feature Preprocessing: The What, the Why, and the How | Matthew Mayo - KDnuggets]

@@ Line 106: / Line 106: @@
 Most feature store APIs provide the following functionality:
-Feature management: Create, read, update, and delete features.
+* <b>Feature management</b>: Create, read, update, and delete features.
-Feature transformation: Preprocess and transform features before serving them to machine learning models.
+* <b>Feature transformation</b>: Preprocess and transform features before serving them to machine learning models.
-Feature serving: Serve features to machine learning models in real time or in batches.
+* <b>Feature serving</b>: Serve features to machine learning models in real time or in batches.
 To use the feature store API, you would typically send HTTP requests to the feature store server. The requests would specify the operation you want to perform (e.g., create a feature, query features, or serve features), as well as the relevant parameters.
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 Here are some additional tips for using a feature store API:
-Use the API documentation to learn about the specific features and functionality that are available.
+* Use the API documentation to learn about the specific features and functionality that are available.
-Start by using the API to perform basic operations, such as creating and reading features.
+* Start by using the API to perform basic operations, such as creating and reading features.
-Once you have a good understanding of the API, you can start using it to perform more complex operations, such as transforming and serving features.
+* Once you have a good understanding of the API, you can start using it to perform more complex operations, such as transforming and serving features.
-If you have any questions or problems using the API, contact the feature store provider for support.
+* If you have any questions or problems using the API, contact the feature store provider for support.
 = Feature Exploration =

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-Offerings:
+=== Offerings ===
 * <b>Continual Feature Store</b>: Open source, designed for real-time machine learning
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-Use Case: You could store these features in a feature store. This would make it easy to reuse the features for different machine learning models, and it would also make it easier to manage the features over time. For example, if you wanted to add a new feature, such as the student's participation in math class, you could simply add it to the feature store.
+== Use Case ==
 Here are some other examples of when you might use a feature store:
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 * You are building a machine learning model to detect fraud. You could use a feature store to store features such as the customer's transaction history, their device information, and their location.
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-* <b>FEAST</b>: is a feature store that is open-source and cloud-native. It is designed to be scalable and performant, and it can be deployed on a variety of cloud platforms. FEAST provides a number of features, including:
+Offerings:
-* A central repository for features, with support for versioning and lineage tracking
+* <b>Continual Feature Store</b>: Open source, designed for real-time machine learning
-* A variety of data processing and transformation capabilities
+* <b>Databricks Feature Store</b>: Fully integrated with Databricks
-* A low-latency serving API for online inference
+* <b>FEAST</b>: Open source, cloud-native, scalable and performant
-* A batch API for offline training and batch predictions
+* <b>FeatureBase</b>: Commercial, offered by Google Cloud, easy to use
-* <b>FeatureBase</b>:  is a commercial feature store that is offered by Google Cloud. It is designed to be easy to use and manage, and it provides a number of features, including:
+Use Case: You could store these features in a feature store. This would make it easy to reuse the features for different machine learning models, and it would also make it easier to manage the features over time. For example, if you wanted to add a new feature, such as the student's participation in math class, you could simply add it to the feature store.
-* A central repository for features, with support for versioning and lineage tracking
+Here are some other examples of when you might use a feature store:
+* You are building a machine learning model to predict whether a customer will churn (cancel their subscription). You could use a feature store to store features such as the customer's past purchase history, their engagement with your product, and their support tickets.
-* <b>feathr</b>:  is a commercial feature store that is offered by Amazon Web Services (AWS). It is designed to be scalable and performant, and it provides a number of features, including:
+* You are building a machine learning model to recommend products to customers. You could use a feature store to store features such as the customer's past purchase history, their browsing history, and their product ratings.
+* You are building a machine learning model to detect fraud. You could use a feature store to store features such as the customer's transaction history, their device information, and their location.

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-<youtube>ZKaOfJIjMRg</youtube>
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← Older revision		Revision as of 00:46, 19 September 2023
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	A feature is an individual measurable property or characteristic of a phenomenon being observed. The concept of a “feature” is related to that of an explanatory variable, which is used in statistical techniques such as linear regression. Feature vectors combine all of the features for a single row into a numerical vector. Part of the art of choosing features is to pick a minimum set of independent variables that explain the problem. If two variables are highly correlated, either they need to be combined into a single feature, or one should be dropped. Sometimes people perform principal component analysis to convert correlated variables into a set of linearly uncorrelated variables. Some of the transformations that people use to construct new features or reduce the dimensionality of feature vectors are simple. For example, subtract Year of Birth from Year of Death and you construct Age at Death, which is a prime independent variable for lifetime and mortality analysis. In other cases, feature construction may not be so obvious. [https://www.infoworld.com/article/3394399/machine-learning-algorithms-explained.html Machine learning algorithms explained \| Martin Heller - InfoWorld]		A feature is an individual measurable property or characteristic of a phenomenon being observed. The concept of a “feature” is related to that of an explanatory variable, which is used in statistical techniques such as linear regression. Feature vectors combine all of the features for a single row into a numerical vector. Part of the art of choosing features is to pick a minimum set of independent variables that explain the problem. If two variables are highly correlated, either they need to be combined into a single feature, or one should be dropped. Sometimes people perform principal component analysis to convert correlated variables into a set of linearly uncorrelated variables. Some of the transformations that people use to construct new features or reduce the dimensionality of feature vectors are simple. For example, subtract Year of Birth from Year of Death and you construct Age at Death, which is a prime independent variable for lifetime and mortality analysis. In other cases, feature construction may not be so obvious. [https://www.infoworld.com/article/3394399/machine-learning-algorithms-explained.html Machine learning algorithms explained \| Martin Heller - InfoWorld]
		+
		+	== Feature Examples ==
		+
		+	For example, if you were building a machine learning model to predict whether someone would like a particular movie, you might use features like the person's age, gender, and favorite genres of movies. You might also use features about the movie itself, such as the genre, director, and rating. Features are important because they allow machine learning models to learn about the world. By providing models with features, we can teach them to identify patterns and make predictions.
		+
		+	Here is an example of a feature in AI that a 7th grader might understand:
		+
		+	Imagine you are building a machine learning model to predict whether a student will pass or fail a math test. You might use the following features:
		+
		+	* The student's grades on previous math tests
		+	* The student's attendance record in math class
		+	* The student's homework completion rate
		+	* The student's score on the math portion of the standardized test
		+
		+	Your machine learning model would learn to identify patterns in this data. For example, the model might learn that students who have high grades on previous math tests and good attendance are more likely to pass the test. The model could also learn that students who miss a lot of class or have incomplete homework are more likely to fail the test. Once your machine learning model is trained, you can use it to predict whether a new student is likely to pass or fail the math test. You can do this by providing the model with the student's features, such as their grades on previous math tests and their attendance record. The model will then use this information to make a prediction.
		+
		+	== Feature Store ==
		+	A feature store in AI is a system for managing and serving features to machine learning models. Features are measurable pieces of data that can be used to train and evaluate models. Feature stores provide a central repository for features, making them easier to discover, reuse, and manage. Feature stores are important because they can help to improve the quality, efficiency, and scalability of machine learning development and deployment. For example, feature stores can help to:
		+
		+	* Reduce the time and effort required to develop and maintain machine learning models
		+	* Improve the performance and accuracy of machine learning models
		+	* Make machine learning models more reproducible and scalable
		+	* Ensure that machine learning models are using consistent and up-to-date data
		+
		+
		+	* <b>FEAST</b>: is a feature store that is open-source and cloud-native. It is designed to be scalable and performant, and it can be deployed on a variety of cloud platforms. FEAST provides a number of features, including:
		+
		+	* A central repository for features, with support for versioning and lineage tracking
		+	* A variety of data processing and transformation capabilities
		+	* A low-latency serving API for online inference
		+	* A batch API for offline training and batch predictions
		+
		+
		+	* <b>FeatureBase</b>: is a commercial feature store that is offered by Google Cloud. It is designed to be easy to use and manage, and it provides a number of features, including:
		+
		+	* A central repository for features, with support for versioning and lineage tracking
		+	* Automatic feature discovery and generation
		+	* A low-latency serving API for online inference
		+	* A batch API for offline training and batch predictions
		+
		+
		+	* <b>feathr</b>: is a commercial feature store that is offered by Amazon Web Services (AWS). It is designed to be scalable and performant, and it provides a number of features, including:
		+
		+	* A central repository for features, with support for versioning and lineage tracking
		+	* Automatic feature generation and transformation
		+	* A low-latency serving API for online inference
		+	* A batch API for offline training and batch predictions
		+
		+
		+	How each fits:
		+
		+	* FEAST is a good choice for organizations that want a flexible and scalable feature store that they can deploy and manage on their own.
		+	* FeatureBase is a good choice for organizations that want a feature store that is easy to use and manage, and that provides integration with Google Cloud Platform services.
		+	* feathr is a good choice for organizations that want a feature store that is scalable and performant, and that provides integration with Amazon Web Services (AWS) services.
		+

	{\|<!-- T -->		{\|<!-- T -->

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 * [[Data Quality]] ...[[AI Verification and Validation|validity]], [[Evaluation - Measures#Accuracy|accuracy]], [[Data Quality#Data Cleaning|cleaning]], [[Data Quality#Data Completeness|completeness]], [[Data Quality#Data Consistency|consistency]], [[Data Quality#Data Encoding|encoding]], [[Data Quality#Zero Padding|padding]], [[Data Quality#Data Augmentation, Data Labeling, and Auto-Tagging|augmentation, labeling, auto-tagging]], [[Data Quality#Batch Norm(alization) & Standardization| normalization, standardization]], and [[Data Quality#Imbalanced Data|imbalanced data]]
 * [[Evaluating Machine Learning Models]]
-* [[Singularity]] ... [[Artificial Consciousness / Sentience|Sentience]] ... [[Artificial General Intelligence (AGI)| AGI]] ... [[Inside Out - Curious Optimistic Reasoning| Curious Reasoning]] ... [[Emergence]] ... [[Moonshots]] ... [[Explainable / Interpretable AI|Explainable AI]] ... [[Algorithm Administration#Automated Learning|Automated Learning]]
+* [[Artificial General Intelligence (AGI) to Singularity]] ... [[Inside Out - Curious Optimistic Reasoning| Curious Reasoning]] ... [[Emergence]] ... [[Moonshots]] ... [[Explainable / Interpretable AI|Explainable AI]] ...  [[Algorithm Administration#Automated Learning|Automated Learning]]
 * [[Recursive Feature Elimination (RFE)]]
 * [[Principal Component Analysis (PCA)]]

@@ Line 28: / Line 28: @@
 * [[Representation Learning]]
 * [[Natural Language Processing (NLP)#Managed Vocabularies |Managed Vocabularies]]
-* [[Excel]] ... [[LangChain#Documents|Documents]] ... [[Database]] ... [[Graph]] ... [[LlamaIndex]]
+* [[Excel]] ... [[LangChain#Documents|Documents]] ... [[Database|Database; Vector & Relational]] ... [[Graph]] ... [[LlamaIndex]]
 * [[Analytics]] ... [[Visualization]] ... [[Graphical Tools for Modeling AI Components|Graphical Tools]] ... [[Diagrams for Business Analysis|Diagrams]] & [[Generative AI for Business Analysis|Business Analysis]] ... [[Requirements Management|Requirements]] ... [[Loop]] ... [[Bayes]] ... [[Network Pattern]]
 * [[Development]] ... [[Notebooks]] ... [[Development#AI Pair Programming Tools|AI Pair Programming]] ... [[Codeless Options, Code Generators, Drag n' Drop|Codeless, Generators, Drag n' Drop]] ... [[Algorithm Administration#AIOps/MLOps|AIOps/MLOps]] ... [[Platforms: AI/Machine Learning as a Service (AIaaS/MLaaS)|AIaaS/MLaaS]]