{"id":382,"date":"2018-09-15T22:52:22","date_gmt":"2018-09-16T04:52:22","guid":{"rendered":"http:\/\/www.jacobsoft.com.mx\/?p=382"},"modified":"2020-03-27T22:48:50","modified_gmt":"2020-03-28T04:48:50","slug":"arboles-de-regresion-usando-python","status":"publish","type":"post","link":"https:\/\/www.jacobsoft.com.mx\/en\/arboles-de-regresion-usando-python\/","title":{"rendered":"Regression trees using Python"},"content":{"rendered":"

Regression trees using Python<\/h2>\n\n\n\n

In data mining, machine learning<\/strong><\/a> and \/ or data science, in what refers to the analysis with trees, there are two main approaches: the decision trees<\/em><\/strong> Y the regression trees<\/em><\/strong>.<\/p>\n\n\n\n

In both cases the trees constitute predictive methods of segmentation<\/strong>, known as classification trees<\/strong>. They are sequential partitions of the data set made to maximize the differences of the dependent variable given that a division of the cases into groups is carried out.<\/p>\n\n\n\n

Through different indices and statistical procedures<\/a><\/strong> the most discriminating division among the selected criteria is determined, the one that allows the different groups to be better differentiated from the base criterion, thus obtaining a first segmentation. From that first segmentation, new segmentations are made of each of the resulting segments and so on until the process ends with some statistical rule.<\/p>\n\n\n\n

Suppose now that we want to know which passengers of the Titanic were more likely to survive its sinking and what characteristics were associated with the survival of the shipwreck. In this case, the variable of interest (GS) is the degree of survival<\/strong>.<\/p>\n\n\n\n

We could then divide the passengers into groups by age<\/strong>, sex<\/strong>, class<\/strong> in which they traveled and observe the proportion of survivors of each group.<\/p>\n\n\n\n

A procedure based on trees<\/a><\/strong>, automatically selects the homogeneous groups with the greatest difference in the proportion of survivors among them. In the first case, sex (men and women).<\/p>\n\n\n\n

The next step consists in subdividing each group of men and women according to other characteristics. As a result, men are divided into adults and children, while women are divided into groups based on the class in which they traveled.<\/p>\n\n\n\n

When the subdivision process is completed, the result is a set of rules that can be easily visualized by a tree<\/a><\/strong>.<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

With the representation of the previous figure we can observe, for example, that if a passenger is male and is an adult, then he has a 20% chance of survival.<\/p>\n\n\n\n

The proportion of survival in each of the subdivisions can be used for purposes predictive<\/a><\/strong> <\/a>to predict the degree of survival of the members of that group.<\/p>\n\n\n\n

Using different predictors (independent variables) at each level of the division process represents a simple and elegant way to handle iterations.<\/p>\n\n\n\n

The procedure creates a classification model based on trees<\/strong><\/a>, and classifies cases into groups or forecasts values \u200b\u200bof a dependent variable based on the values \u200b\u200bof the independent variables.<\/p>\n\n\n\n