我在客户支持方面工作,并且我正在使用scikit-learn来预测我们门票的标签,给出一套训练门票(训练集中大约40,000张门票).
我正在使用基于此的分类模型.它只是预测"()"作为我的许多测试票集的标签,即使训练集中没有一张票没有标签.
我的标签培训数据是一个列表列表,例如:
tags_train = [['international_solved'], ['from_build_guidelines my_new_idea eligibility'], ['dropbox other submitted_faq submitted_help'], ['my_new_idea_solved'], ['decline macro_backer_paypal macro_prob_errored_pledge_check_credit_card_us loading_problems'], ['dropbox macro__turnaround_time other plq__turnaround_time submitted_help'], ['dropbox macro_creator__logo_style_guide outreach press submitted_help']]
虽然我的故障单描述的培训数据只是一个字符串列表,例如:
descs_train = ['description of ticket one', 'description of ticket two', etc]
这是构建模型的代码的相关部分:
import numpy as np
import scipy
from sklearn.pipeline import Pipeline
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.multiclass import OneVsRestClassifier
from sklearn.svm import LinearSVC
# We have lists called tags_train, descs_train, tags_test, descs_test with the …