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Regression & Log linear models

Regression models can be used to approximate the given data. The data are modeled to fit a straight line.

 y = wx + b

where 

y  - response variable

x  - predictor variable

w , b  - regression coefficients

The coefficient specify the slope of the line and y intercept. The method of least squares shall be used to solve the coefficients.

Log linear models approximate discrete multidimensional probability distributions. Log-linear models can be used to estimate the probability of each point in a multidimensional space for a set of discretized attributes, based on a smaller subset of dimensional combinations. This allows a higher-dimensional data space to be constructed from lower-dimensional spaces.

Regression and log-linear models can both be used on sparse data and skewed data too. Regression can be computationally intensive when applied to high-dimensional data.

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