a7-naive-bayes-TryAnything-K1sh1 created by GitHub Classroom, stars:0, update:2024-04-12 19:13:53 (2024-04-13, Python, 0KB, 下载0次)
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GitHub Classroom创建的a7-naive-bayes-redo-wo4455
a7-naive-bayes-redo-wo4455 created by GitHub Classroom (2024-04-09, Python, 0KB, 下载0次)
GitHub Classroom创建的a7朴素贝叶斯重做AR CPS
a7-naive-bayes-redo-AR-CPS created by GitHub Classroom (2024-04-09, Python, 0KB, 下载0次)
创建了一个Naive baye的文本分类模型，对垃圾邮件和火腿进行分类，准确率为97%。
Created a Naive baye s model for text classification that classifies the spam emails and ham with 97% accuracy. (2024-03-10, Jupyter Notebook, 0KB, 下载0次)
使用多项式朴素贝叶斯算法实现了监督文本分类，总体准确率达到97%。部署了mo...
Implemented a Supervised Text Classification using Multinomial Naive Bayes algorithm achieved an overall accuracy of 97%. Deployed the model into a web application using flask and hosted it on Python Anywhere Platform. Website link - http://ashi2003.pythonanywhere.com/ (2023-11-17, Jupyter Notebook, 0KB, 下载0次)
所使用的监督机器学习技术是支持向量机，它帮助我们给出了97.11%的准确率。，
The supervised Machine learning Technique used is Support Vector Machine which help us give an accuracy of 97.11% ., (2023-10-15, Jupyter Notebook, 0KB, 下载0次)
使用Bert进行文档分类| 97%准确性，
Document Classification using Bert | 97 % accuracy, (2023-10-07, Jupyter Notebook, 0KB, 下载0次)
A1.支持向量机，，
A1.Support Vector Machines,, (2023-09-24, Jupyter Notebook, 0KB, 下载0次)
开发了一个机器学习模型，用于对垃圾邮件和合法（ham）电子邮件进行准确分类，有助于97%的检测...，
Developed a machine learning model for accurate classification of spam and legitimate (ham) emails, contributing to a 97 percent detection accuracy. Used maximum voting classifier for best results. (2023-08-27, Jupyter Notebook, 0KB, 下载0次)
SVM精度：97.78%K均值精度：33.33%C50精度：97.7%，
SVM accuracy: 97.78% K Means accuracy: 33.33% C50 accuracy: 97.78%, (2023-08-10, Others, 0KB, 下载0次)
SVM精度：97.78%K均值精度：89.33%C50精度：97.7%，
SVM accuracy: 97.78% K Means accuracy: 89.33% C50 accuracy: 97.78%, (2023-08-10, R, 0KB, 下载0次)
我在这个项目中使用了随机森林分类器模型，并实现了97.9%的测试准确性。
I Have used the Random Forest Classifier model in this project and have achieved a testing accuracy of 97.9%. (2020-08-02, Python, 146KB, 下载0次)
随机森林具有最高的98%的精度，若采用相同的标记数，ROC_AUC曲线97%的模型可以得到更好的改进...
Random forest has highest accuracy 98% and ROC_AUC curve 97% model can be improve more if we take same count of labels in our model 30% is diabetic and 70% no diabetic patient (2023-01-19, Jupyter Notebook, 3807KB, 下载0次)
KNN，Logistic回归
KNN, Logistic Regression (2015-10-22, Python, 113KB, 下载0次)
使用KNN算法从手写数字中检测数字的Python模型，精度高达97%
Python model which detects number from handwritten digits with upto 97% accuracy using KNN algorithm (2022-07-05, Jupyter Notebook, 13235KB, 下载0次)
该django项目旨在使用机器学习对脑肿瘤进行检测和分类，其诊断准确率为97.554%...
This django project aims to detect and classify brain tumors using machine learning with an accuracy of 97.554% for detection and 95.3% for classification. then retrieve 5 similar cases (2022-07-17, JavaScript, 17341KB, 下载0次)
编程实践网络集群A1
Praktikum Pemograman Web Cluster A1 (2020-12-14, HTML, 3KB, 下载0次)
使用Javascript进行数据聚类的代码-Medium：[https:media.com@joaogabriellima clusteriza%C3%A7%C3%A3o de-dad...]
Codes of Data Clustering with Javascript - Medium: <https://medium.com/@joaogabriellima/clusteriza%C3%A7%C3%A3o-de-dados-com- javascript-parte-1-conceitos-fundamentais-c7a676e70a4b> (2017-11-26, JavaScript, 12KB, 下载0次)
Terraform在Ampere A1处理器上提供OCI OKE集群，然后在其上部署nginx
Terraform to provision an OCI OKE cluster on Ampere A1 Processors, and then deploy nginx on it (2022-12-10, HCL, 127KB, 下载0次)
本科生小组项目，我们使用TCN-CNN构建了一个心电图分类器，准确率为97%
Undergraduate group project in which we built an ECG classifier using a TCN- CNN with 97% accuracy (2021-10-02, Jupyter Notebook, 27553KB, 下载0次)