Nonparametric regression | Regression models

In statistics, an additive model (AM) is a nonparametric regression method. It was suggested by Jerome H. Friedman and Werner Stuetzle (1981) and is an essential part of the ACE algorithm. The AM uses a one-dimensional smoother to build a restricted class of nonparametric regression models. Because of this, it is less affected by the curse of dimensionality than e.g. a p-dimensional smoother. Furthermore, the AM is more flexible than a standard linear model, while being more interpretable than a general regression surface at the cost of approximation errors. Problems with AM, like many other machine learning methods, include model selection, overfitting, and multicollinearity. (Wikipedia).

Linear regression is used to compare sets or pairs of numerical data points. We use it to find a correlation between variables.

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From playlist Machine Learning

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From playlist Statistics

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From playlist Solving Applications Using Exponential Equations / Compounded and Continuous Interest / Exponential Regression

An Introduction to Linear Regression Analysis

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From playlist Linear Regression.

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From playlist Statistics

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From playlist Introduction to Exponential Functions

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From playlist MIT 18.217 Graph Theory and Additive Combinatorics, Fall 2019

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