using Multiple Linear regression ( R_squared_score = 0.97), stars:0, update:2024-04-20 17:12:29 (2024-04-22, Jupyter Notebook, 0KB, 下载0次)
http://www.pudn.com/Download/item/id/1713792460164695.html
全面的乳腺癌检测ML存储库，具有一套算法，包括Logistic回归、KNN、随机森林、决策树和SVC，通过GridSearchCV进行优化。在肿瘤分类中实现了开创性的97.66%的准确性，展示了先进的机器学习技术在医疗诊断中的威力
Comprehensive Breast Cancer Detection ML repository featuring a suite of algorithms including Logistic Regression, KNN, Random Forest, Decision Tree, and SVC, optimized through GridSearchCV. Achieved a groundbreaking 97.66% accuracy in tumor classification, demonstrating the power of advanced machine learning techniques in healthcare diagnostics (2024-02-06, Jupyter Notebook, 0KB, 下载0次)
使用Python实现了一个垃圾邮件检测项目，采用了数据清理、探索性数据分析和文本预处理技术。训练和评估朴素贝叶斯模型，使用贝努利朴素贝叶分类器实现了显著的97%准确性和97.35%精度。
Implemented a spam detection project using Python, employing data cleaning, exploratory data analysis, and text preprocessing techniques. Trained and evaluated Naive Bayes models, achieving a notable 97% accuracy and 97.35% precision with the Bernoulli Naive Bayes classifier. (2024-01-02, Jupyter Notebook, 0KB, 下载0次)