Regression

Target entity: Regression
Context triple: [Alejandro Amenábar, notableWork, Regression]

• A. LogisticRegression
  LogisticRegression is a scikit-learn machine learning estimator that models the probability of class membership using a linear decision boundary with logistic (sigmoid) or related link functions.
• B. Bayesian linear regression
  Bayesian linear regression is a statistical modeling approach that treats regression coefficients and predictions probabilistically by placing prior distributions on parameters and updating them with observed data.
• C. Linear Estimation
  Linear Estimation is a foundational text in signal processing and control theory that systematically develops the theory and applications of optimal estimation, including Kalman filtering and related methods.
• D. PredictionEngine
  PredictionEngine is an ML.NET API component that provides a simple, strongly typed interface for making single-record predictions with trained machine learning models in .NET applications.
• E. Gaussian process
  A Gaussian process is a collection of random variables indexed by a set (often time or space) such that every finite subset has a joint multivariate normal distribution, widely used to model functions in probability theory and machine learning.
• F. None of above. chosen
• G. Unsure - the case is ambiguous/there is not enough information to decide.

Target entity: Regression
Target entity description: Regression is a 2015 psychological thriller film directed by Alejandro Amenábar, centered on a detective investigating a disturbing case of alleged satanic ritual abuse in 1990s Minnesota.

• A. LogisticRegression
  LogisticRegression is a scikit-learn machine learning estimator that models the probability of class membership using a linear decision boundary with logistic (sigmoid) or related link functions.
• B. Bayesian linear regression
  Bayesian linear regression is a statistical modeling approach that treats regression coefficients and predictions probabilistically by placing prior distributions on parameters and updating them with observed data.
• C. Linear Estimation
  Linear Estimation is a foundational text in signal processing and control theory that systematically develops the theory and applications of optimal estimation, including Kalman filtering and related methods.
• D. PredictionEngine
  PredictionEngine is an ML.NET API component that provides a simple, strongly typed interface for making single-record predictions with trained machine learning models in .NET applications.
• E. Gaussian process
  A Gaussian process is a collection of random variables indexed by a set (often time or space) such that every finite subset has a joint multivariate normal distribution, widely used to model functions in probability theory and machine learning.
• F. None of above. chosen