> For the complete documentation index, see [llms.txt](https://r-pedia.gitbook.io/cevi/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://r-pedia.gitbook.io/cevi/machine-learning-1/1.-supervised-learning.md).

# 1. Supervised Learning

**How it works:** This algorithm consist of a target / outcome variable (or dependent variable) which is to be predicted from a given set of predictors (independent variables). Using these set of variables, we generate a function that map inputs to desired outputs. The training process continues until the model achieves a desired level of accuracy on the training data.

## -Linear Regression:

Different techniques can be used to learn the linear regression model from data, such as a linear algebra solution for **ordinary least squares** and **gradient descent optimization**.

 Some good rules of thumb when using this technique are to **remove variables that are very similar (correlated)** and to **remove noise from your data**.

More:

* <https://www.machinelearningplus.com/machine-learning/complete-introduction-linear-regression-r/>
* <http://r-statistics.co/Linear-Regression.html>
*

## -Non linear Regression:

Examples of classification algorithms include:

* [Linear classifiers](https://en.wikipedia.org/wiki/Linear_classifier)
  * [Fisher's linear discriminant](https://en.wikipedia.org/wiki/Fisher%27s_linear_discriminant)
  * [Logistic regression](https://en.wikipedia.org/wiki/Logistic_regression)
  * [Naive Bayes classifier](https://en.wikipedia.org/wiki/Naive_Bayes_classifier)
  * [Perceptron](https://en.wikipedia.org/wiki/Perceptron)
* [Support vector machines](https://en.wikipedia.org/wiki/Support_vector_machine)
  * [Least squares support vector machines](https://en.wikipedia.org/wiki/Least_squares_support_vector_machine)
* [Quadratic classifiers](https://en.wikipedia.org/wiki/Quadratic_classifier)
* [Kernel estimation](https://en.wikipedia.org/wiki/Variable_kernel_density_estimation#Use_for_statistical_classification)
  * [k-nearest neighbor](https://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm)
* [Boosting (meta-algorithm)](https://en.wikipedia.org/wiki/Boosting_\(meta-algorithm\))
* [Decision trees](https://en.wikipedia.org/wiki/Decision_tree_learning)
  * [Random forests](https://en.wikipedia.org/wiki/Random_forest)
* [Neural networks](https://en.wikipedia.org/wiki/Artificial_neural_networks)
* [Learning vector quantization](https://en.wikipedia.org/wiki/Learning_vector_quantization)