分类问题的准确性得分差 [英] poor accuracy score on classfication problem
问题描述
我正在尝试建立分类模型，并且我的目标不是二进制.我的功能与目标的相关性都很弱(大多为0.1).我已经对数据进行了预处理，并对其应用了所有算法(我使用的算法为svm, knn, naivebayes,logistic regression, decision tree,gradient boosting, random forest).我用sklearn metrics.accuracy_score评估了所有模型，只是想知道它们在我的数据上的表现如何，但是他们的得分都在0.1〜0.2之间.目标是productline列.
I'm trying to build a classification model and my target is not binary. The correlations of my features against my target are all weak (mostly 0.1). I have preprocessed my data and applied the all the algorithms i used to it (the algorithms i used are svm, knn, naivebayes,logistic regression, decision tree,gradient boosting, random forest). I evaluated all of the models with sklearn metrics.accuracy_score just to know how good they perform on my data but all of them scored 0.1~0.2 . The target is productline column.
我的问题
- 这怎么可能?
- 如何解决此问题?
- 还有其他算法可以取得更好的成绩吗?
推荐答案
What's the accuracy if you use a dummy classifier? The accuracy of the models you have tried should be at least equal to that of the dummy classifier.
这怎么可能?"如果功能和目标变量之间没有关系，则该模型将不会返回良好的结果.
"How could this happen?" If there's no relationship between the features and the target variable, the model isn't going to return good results.
我不确定您的数据集的详细信息，但是您可以尝试1)获取更多数据2)获取更多特征3)做一些特征工程4)清理数据集(如果没有的话)异常值或错误输入会影响您的结果
I'm not sure about the details of your dataset, but you can try to 1) Get more data 2) Get more features 3) Do some feature engineering 4) Clean your dataset if you haven't, there might be outliers or wrong inputs affecting your results
这篇关于分类问题的准确性得分差的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
