Hi, I`m a graduate student utilizing sklean for some data work. And when I`m handling the data using the Decision Trees library, I found there are some inconvenience: Neither the classificationTree nor the regressionTree supports categorical feature. That means the Decision trees model can only accept continuous feature. For example, the categorical feature like app name such as google, facebook can`t be input into the model, because they can`t be transformed to continuous value properly. And there don`t exist a corresponding algorithm to divide discrete feature in the Decision Trees library. However, the CART algorithm itself has considered the use of categorical feature. So I have made some modification of Decision Trees library based on CART and apply the new model on my own work. And it proves that the support for categorical feature indeed improves the performance, which is very necessary for decision tree, I think. I`m very willing to contribute this to sklearn community, but I`m new to this community, not so familiar about the procedure. Could u give some suggestions or comments on this new feature? Or has anyone already processed on this feature? Thank you so much.
Best wishes! -- 顺颂时祺！ 李扬 上海交通大学 电子信息 与 电气工程 学院 电话：18818212371 地址：上海市闵行区东川路800号 邮编：200240 Yang Li +86 188 1821 2371 Shanghai Jiao Tong University School of Electronic，Information and Electrical Engineering F1203026 800 Dongchuan Road, Minhang District, Shanghai 200240
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