《Python Machine Learning》笔记（1）

机器学习分为：unsupervised learning， supervised learning 和 Reinforcement learning

 

Supervised Learning （监督学习）

The main goal in supervised learning is to learn to model from labeled trainning data that allows us to make predictions about unseen or future data. Here the term supervised to a set of samples where the desired output signals (labels) are already known.

 

监督学习可分为两类：

A supervised learning task with discrete(离散的) class labels, is also called a classification task.

Another subcategory of supervised learning is regression, where the outcome signals is a continuous value.

 

流程：

                            Training Data (with Labels)

                                               |

                            Machine Learning Algorithm

                                               |

               New Data -> Predivtive Model -> Prediction 

 

Classification

The goal is to predict the categorical class labels of new instances based on past observations. Thoese class labels are discrete, unordered values that can be understood as the group memberships of the instances. Then the machine learning algorithm learns a set of rules in order to distinguish between two (or more than two) possible classes.