# [−][src]Crate vikos

A machine learning library for supervised regression and classifaction

This library wants to enable its users to write models independently of the teacher used for training or the cost function that is meant to be minimized. To get started right away, you may want to have a look at the tutorial.

## Modules

cost | Implementations of |

crisp | Contains implementations for crisp trait |

linear_algebra | Defines linear algebra traits used for some model parameters |

model | Implementations of |

teacher | Learning algorithms implementing |

tutorial | A short tutorial on how to use vikos to solve the problem of supervised machine learning: We
want to predict values for a quantity (the target), and we have some data that we can base our
inference on (features). We have a data set (a history), that consists of features and
corresponding, |

## Traits

Cost | Representing a cost function whose value is supposed be minimized by the training algorithm. |

Crisp | Define this trait over the target type of a classifier, to convert it into its truth type |

Model | A parameterized expert algorithm |

Teacher | Algorithms used to adapt Model coefficients |

## Functions

learn_history | Teaches |