# .css-4zleql{display:block;}std::steam  # 100 Days Of ML Code — Day 013

Jehoshaphat I. Abu
·Jul 22, 2018·

## Recap from Day 012

In day 012 we started looking at Common Regression Algorithms, and we touched on Linear Regression and Nonlinear Regression.

Today we will continue with Common Regression Algorithms.

## Common Regression Algorithms continued

### Gaussian Process Regression Model

“Gaussian process regression (GPR) models are nonparametric models that are used for predicting the value of a continuous response variable. They are widely used in the field of spatial analysis for interpolation in the presence of uncertainty.” Source: MathWorks- 90221_80827v00_machine_learning_section4_ebook_v03 pdf**

Best Used…

• For interpolating spatial data, such as hydrogeological data for the distribution of ground water

• As a surrogate model to facilitate optimization of complex designs such as automotive engines

### SVM Regression

The Support Vector Regression (SVR) uses the same principles as the SVM for classification, with only a few minor differences. They are modified to be able to predict a continuous response.

“Instead of finding a hyperplane that separates data, SVM regression algorithms find a model that deviates from the measured data by a value no greater than a small amount, with parameter values that are as small as possible (to minimize sensitivity to error)”. Source: MathWorks- 90221_80827v00_machine_learning_section4_ebook_v03 pdf**

Best Used…

• For high-dimensional data (where there will be a large number of predictor variables)

You made it to the end of day 013. I hope you found this informative. Thank you for taking time out of your schedule and allowing me to be your guide on this journey.

Reference

MathWorks- 90221_80827v00_machine_learning_section4_ebook_v03 pdf