ABC
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FilenameDemos featured inDescription
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ml_regression_ARDdemo_regression_ARDFit a linear model using Automatic Relevance Determination / Sparse Bayesian Learning using the fixed-point method of MacKay
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ml_regression_baggingdemo_regression_baggingFits a regression algorithm on bootstrap samples of the dataset and predicts based on the average over all bootstrapped models
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ml_regression_basisdemo_regression_regression_CVFits a linear regression model under a change of basis
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ml_regression_GAMdemo_regression_GAMFits a Generalized Additive Model to the dataset using one of cubic splines, polynomial regresion, or linear regression as function to determine f's
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ml_regression_Huberdemo_regression_outliersFits a linear regression model by minimizing the Huber loss function at a specified epsilon
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ml_regression_kerneldemo_regression_kernelFits a linear regression model by finding the weights that minimize the squared loss of a kernelized representation of the dataset
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ml_regression_KNNdemo_regression_nonparamFits a regression model by predicting each response as the average fo the k nearest neighbours to each example in the training data set
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ml_regression_L1demo_regression_ARD, demo_regression_outliersFits a linear regression model by minimizing the sum of absolute errors (L1 loss)
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ml_regression_L2demo_regression_ARD, demo_regression_bagging, demo_regression_basis, demo_regression_NB, demo_regression_outliers, demo_regression_regression_CVFits a linear regression model by minimizing the sum of squared errors (L2 loss)
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ml_regression_localdemo_regression_nonparamFits a local regression model around each point in the training set, using a specified weighting function for the k nearest neighbours of that point
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ml_regression_meandemo_regression_bagging, demo_regression_regressOnOneFits a baseline regression model which always predicts the mean of y
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ml_regression_MLPdemo_regression__MLPFits a neural network of specific architecture with sigmoid or hyperbolic tangent activitation function to the dataset with an identity transform as the final activiation
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ml_regression_NBdemo_regression_NBFits a linear regression model by minimizing the Naive Bayes squared loss
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ml_regression_NWdemo_regression_nonparamFits a Nadaray-Watson (locally weighted KNN-like) kernel regression model by estimating y as a locally weighted average, where the weighting function is a kernel.
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ml_regression_regressOnOnedemo_regression_regressOnOneFits a regression model by minimizing the squared error for one feature in the dataset
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ml_regression_studentdemo_regression_outliersFits linear regression model using Student's t loss function, with optional polynomial basis, L2 regularization, or weights on training examples
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ml_regression_stumpdemo_regression_treeFinds the optimal binary split for a training set and fits a model on each side
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ml_regression_SVRdemo_regression_SVRSupport-vector regression using an epsilon-insensitive loss function
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ml_regression_totalL2NoneFit a linear regression model that allows for errors in the dependent and independent variables.
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ml_regression_treedemo_regression_treeTrain regression model by partitioning feature space into set of rectangular regions, and fitting the constant model or linear model that minimizes the sum of squares in each region.