简体繁体 English

SVM、朴素贝叶斯和随机森林的准确度、精确度、召回率 RMSE 和 MAE 值相同

[英]Accuracy, Precision, Recall RMSE and MAE values are same for SVM, Naive Bayes and Random Forest

原文 2021-02-03 17:37:38 5 1 python/ sentiment-analysis

I am studying on sentiment analysis of tourist reviews.我正在研究旅游评论的情感分析。 90% of reviews are positive the rest are negative. 90% 的评论是正面的，其余的都是负面的。 My Accuracy, Precision, Recall RMSE, and MAE values are the same for SVM, Naive Bayes, and Random Forest.对于 SVM、朴素贝叶斯和随机森林，我的准确度、精确度、召回 RMSE 和 MAE 值是相同的。 Is it possible or is there any mistake?有没有可能或者有什么错误？ Btw, I am using Python.顺便说一句，我正在使用 Python。

Thank you.谢谢你。

1 个解决方案

Try setting average='weighted' for precision/recall and you should see some different values:尝试为精度/召回率设置average='weighted' ，您应该会看到一些不同的值：

https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_score.html https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_score.html

I am not sure it is available for the other metrics.我不确定它是否适用于其他指标。

召回率 = 0 的 SVM 和随机森林 - SVM and Random Forest with recall = 0

确定 H2O 随机森林模型的准确度、精确度、召回率和 F-Score - Determining the accuracy, precision, recall and F-Score of an H2O Random Forest Model

朴素贝叶斯和 SVM 分类 - 如何在 xy 轴上提高 plot 精度？ - Naive Bayes and SVM classification - how to plot accuracy on x y axis?

提高文本分类的准确性（目前，朴素贝叶斯和SVM的分类率达到62％） - Improve accuracy for text categorization (currently getting 62% for Naive Bayes and SVM)

Accuracy 和 Recall 值相同 - Accuracy and Recall values are same

随机森林回归 MAE - Random Forest Regression MAE

获得一个分类报告，说明使用10倍交叉验证的多项式朴素贝叶斯的类精确度和召回率 - Get a classification report stating the class wise precision and recall for multinomial Naive Bayes using 10 fold cross validation

朴素贝叶斯的实施 - 准确性问题 - Implementation of Naive Bayes - accuracy issues

提高准确性朴素贝叶斯分类器 - Improve accuracy Naive Bayes Classifier

情感分析，朴素贝叶斯准确性 - Sentiment Analysis, Naive Bayes Accuracy

暂无

暂无

声明:本站的技术帖子网页，遵循CC BY-SA 4.0协议，如果您需要转载，请注明本站网址或者原文地址。任何问题请咨询:yoyou2525@163.com.

相关问题 召回率 = 0 的 SVM 和随机森林 - SVM and Random Forest with recall = 0 确定 H2O 随机森林模型的准确度、精确度、召回率和 F-Score - Determining the accuracy, precision, recall and F-Score of an H2O Random Forest Model 朴素贝叶斯和 SVM 分类 - 如何在 xy 轴上提高 plot 精度？ - Naive Bayes and SVM classification - how to plot accuracy on x y axis? 提高文本分类的准确性（目前，朴素贝叶斯和SVM的分类率达到62％） - Improve accuracy for text categorization (currently getting 62% for Naive Bayes and SVM) Accuracy 和 Recall 值相同 - Accuracy and Recall values are same 随机森林回归 MAE - Random Forest Regression MAE 获得一个分类报告，说明使用10倍交叉验证的多项式朴素贝叶斯的类精确度和召回率 - Get a classification report stating the class wise precision and recall for multinomial Naive Bayes using 10 fold cross validation 朴素贝叶斯的实施 - 准确性问题 - Implementation of Naive Bayes - accuracy issues 提高准确性朴素贝叶斯分类器 - Improve accuracy Naive Bayes Classifier 情感分析，朴素贝叶斯准确性 - Sentiment Analysis, Naive Bayes Accuracy

相关标签

粤ICP备18138465号 © 2020-2024 STACKOOM.COM