10种经典机器学习算法(python实现)

上传者: m0_46384757 | 上传时间: 2022-05-27 12:05:09 | 文件大小: 5.05MB | 文件类型: RAR
包含的机器学习算法有: 1. 贝叶斯 2. 聚类 3. 决策树 4. 集成学习 5. 线性回归 6. 逻辑回归 7. 神经网络 8. PCA 9. 感知机 10. SVM

文件下载

资源详情

[{"title":"( 68 个子文件 5.05MB ) 10种经典机器学习算法(python实现)","children":[{"title":"machine_learning","children":[{"title":"linear-regression","children":[{"title":"linear_regression_2.py <span style='color:#111;'> 406B </span>","children":null,"spread":false},{"title":"linear_regression_1.py <span style='color:#111;'> 1001B </span>","children":null,"spread":false},{"title":"linear_regression_boston.py <span style='color:#111;'> 1.87KB </span>","children":null,"spread":false}],"spread":true},{"title":"bayes","children":[{"title":"douban_sentiment_analysis-master","children":[{"title":".gitignore <span style='color:#111;'> 1.24KB </span>","children":null,"spread":false},{"title":"README.md <span style='color:#111;'> 2.25KB </span>","children":null,"spread":false},{"title":"native_bayes.ipynb <span style='color:#111;'> 4.74KB </span>","children":null,"spread":false},{"title":"native_bayes_test.py <span style='color:#111;'> 3.13KB </span>","children":null,"spread":false},{"title":"__pycache__","children":[{"title":"native_bayes_sentiment_analyzer.cpython-37.pyc <span style='color:#111;'> 2.89KB </span>","children":null,"spread":false}],"spread":true},{"title":"run_test.py <span style='color:#111;'> 1018B </span>","children":null,"spread":false},{"title":"native_bayes_sentiment_analyzer.py <span style='color:#111;'> 2.31KB </span>","children":null,"spread":false},{"title":"native_bayes_train.py <span style='color:#111;'> 2.41KB </span>","children":null,"spread":false},{"title":"data","children":[{"title":"bayes.pkl <span style='color:#111;'> 1.91MB </span>","children":null,"spread":false},{"title":"userdict.txt <span style='color:#111;'> 703B </span>","children":null,"spread":false},{"title":"review.csv <span style='color:#111;'> 7.07MB </span>","children":null,"spread":false},{"title":"stopwords.txt <span style='color:#111;'> 8.92KB </span>","children":null,"spread":false}],"spread":true}],"spread":true},{"title":"拼写检查器","children":[{"title":"test.py <span style='color:#111;'> 1.85KB </span>","children":null,"spread":false},{"title":"big.txt <span style='color:#111;'> 51.80KB </span>","children":null,"spread":false}],"spread":true}],"spread":true},{"title":"SVM","children":[{"title":"svm_sklearn.py <span style='color:#111;'> 5.54KB </span>","children":null,"spread":false}],"spread":true},{"title":"neural network","children":[{"title":"mlp_neural_network.py <span style='color:#111;'> 2.04KB </span>","children":null,"spread":false},{"title":"activation_function_jupyter.py <span style='color:#111;'> 358B </span>","children":null,"spread":false},{"title":"activation_function.py <span style='color:#111;'> 762B </span>","children":null,"spread":false}],"spread":true},{"title":"ensenmble learning","children":[{"title":"xgboost","children":[{"title":"ensemble","children":[{"title":"horseColicTest.txt <span style='color:#111;'> 13.23KB </span>","children":null,"spread":false},{"title":"Iris.csv <span style='color:#111;'> 4.99KB </span>","children":null,"spread":false},{"title":"lenses.txt <span style='color:#111;'> 771B </span>","children":null,"spread":false},{"title":"horseColicTraining.txt <span style='color:#111;'> 59.06KB </span>","children":null,"spread":false}],"spread":true},{"title":"xgb_Iris.py <span style='color:#111;'> 1.05KB </span>","children":null,"spread":false},{"title":"xgb.py <span style='color:#111;'> 1.09KB </span>","children":null,"spread":false}],"spread":true},{"title":"GBDT_Simple_Tutorial-master","children":[{"title":".gitignore <span style='color:#111;'> 81B </span>","children":null,"spread":false},{"title":"展示图片","children":[{"title":"all_trees.png <span style='color:#111;'> 617.68KB </span>","children":null,"spread":false}],"spread":true},{"title":"README.md <span style='color:#111;'> 1.99KB </span>","children":null,"spread":false},{"title":"GBDT","children":[{"title":"gbdt.py <span style='color:#111;'> 8.35KB </span>","children":null,"spread":false},{"title":"decision_tree.py <span style='color:#111;'> 5.85KB </span>","children":null,"spread":false},{"title":"__pycache__","children":[{"title":"decision_tree.cpython-37.pyc <span style='color:#111;'> 3.80KB </span>","children":null,"spread":false},{"title":"loss_function.cpython-37.pyc <span style='color:#111;'> 6.70KB </span>","children":null,"spread":false},{"title":"gbdt.cpython-37.pyc <span style='color:#111;'> 8.24KB </span>","children":null,"spread":false},{"title":"tree_plot.cpython-37.pyc <span style='color:#111;'> 7.80KB </span>","children":null,"spread":false}],"spread":false},{"title":"loss_function.py <span style='color:#111;'> 5.13KB </span>","children":null,"spread":false},{"title":"tree_plot.py <span style='color:#111;'> 10.49KB </span>","children":null,"spread":false}],"spread":true},{"title":"example.py <span style='color:#111;'> 4.38KB </span>","children":null,"spread":false},{"title":"LICENSE <span style='color:#111;'> 11.08KB </span>","children":null,"spread":false},{"title":"results","children":[{"title":"NO.2_tree.log <span style='color:#111;'> 2.04KB </span>","children":null,"spread":false},{"title":"NO.3_tree.png <span style='color:#111;'> 19.88KB </span>","children":null,"spread":false},{"title":"result.log <span style='color:#111;'> 881B </span>","children":null,"spread":false},{"title":"NO.1_tree.png <span style='color:#111;'> 19.44KB </span>","children":null,"spread":false},{"title":"NO.4_tree.png <span style='color:#111;'> 19.92KB </span>","children":null,"spread":false},{"title":"NO.5_tree.log <span style='color:#111;'> 2.05KB </span>","children":null,"spread":false},{"title":"all_trees.png <span style='color:#111;'> 849.21KB </span>","children":null,"spread":false},{"title":"NO.4_tree.log <span style='color:#111;'> 2.05KB </span>","children":null,"spread":false},{"title":"NO.2_tree.png <span style='color:#111;'> 19.71KB </span>","children":null,"spread":false},{"title":"all_trees_high_quality.png <span style='color:#111;'> 53.25KB </span>","children":null,"spread":false},{"title":"NO.3_tree.log <span style='color:#111;'> 2.05KB </span>","children":null,"spread":false},{"title":"NO.1_tree.log <span style='color:#111;'> 2.05KB </span>","children":null,"spread":false},{"title":"NO.5_tree.png <span style='color:#111;'> 19.86KB </span>","children":null,"spread":false}],"spread":false}],"spread":true}],"spread":true},{"title":"cluster","children":[{"title":"k-means-sklearn.py <span style='color:#111;'> 814B </span>","children":null,"spread":false},{"title":"k-means.py <span style='color:#111;'> 4.45KB </span>","children":null,"spread":false}],"spread":true},{"title":"Decision Tree","children":[{"title":"my_tree.py <span style='color:#111;'> 521B </span>","children":null,"spread":false},{"title":"gini_cul.py <span style='color:#111;'> 446B </span>","children":null,"spread":false},{"title":"decision_tree_sklearn.py <span style='color:#111;'> 1.06KB </span>","children":null,"spread":false}],"spread":true},{"title":"PCA","children":[{"title":"PCA.py <span style='color:#111;'> 2.93KB </span>","children":null,"spread":false},{"title":"PCA_sklearn.py <span style='color:#111;'> 1.66KB </span>","children":null,"spread":false},{"title":"test.py <span style='color:#111;'> 101B </span>","children":null,"spread":false}],"spread":true},{"title":"perceptron感知机","children":[{"title":"perceptron.py <span style='color:#111;'> 3.34KB </span>","children":null,"spread":false},{"title":"perceptron_sklearn.py <span style='color:#111;'> 885B </span>","children":null,"spread":false}],"spread":true},{"title":"logistics-regression","children":[{"title":"test.py <span style='color:#111;'> 167B </span>","children":null,"spread":false},{"title":"logistics_regression2.py <span style='color:#111;'> 1.65KB </span>","children":null,"spread":false},{"title":"sigmoid.py <span style='color:#111;'> 210B </span>","children":null,"spread":false},{"title":"logistics_regression1.py <span style='color:#111;'> 2.38KB </span>","children":null,"spread":false},{"title":"正则化(regularization).py <span style='color:#111;'> 443B </span>","children":null,"spread":false}],"spread":true}],"spread":true}],"spread":true}]

评论信息

免责申明

【只为小站】的资源来自网友分享,仅供学习研究,请务必在下载后24小时内给予删除,不得用于其他任何用途,否则后果自负。基于互联网的特殊性,【只为小站】 无法对用户传输的作品、信息、内容的权属或合法性、合规性、真实性、科学性、完整权、有效性等进行实质审查;无论 【只为小站】 经营者是否已进行审查,用户均应自行承担因其传输的作品、信息、内容而可能或已经产生的侵权或权属纠纷等法律责任。
本站所有资源不代表本站的观点或立场,基于网友分享,根据中国法律《信息网络传播权保护条例》第二十二条之规定,若资源存在侵权或相关问题请联系本站客服人员,zhiweidada#qq.com,请把#换成@,本站将给予最大的支持与配合,做到及时反馈和处理。关于更多版权及免责申明参见 版权及免责申明