搜索资源列表
scikit_learn-0.19.1-cp27-cp27m-win32.whl
- sklearn，机器学习算法，用于创建算法模型以及预处理数据(Sklearn, a machine learning algorithm, is used to create a model of the algorithm and data preprocessing)
scipy-1.0.0-cp27-none-win32.whl
- for python2.7 用于计算机深度学习，被sklearn依赖(For computer deep learning, is sklearn dependent)
svm
- 结合数字分类实例代码，学习sklearn中svm函数库的使用，完成简单的分类任务(Learn the use of the SVM function library in sklearn with the digital classification example code to complete a simple classification task)
47016
- SKLearn 的ＳＶＣ工具包，用于测试训练样本集的练习(The tools for SKLearn SVC)
scikit-learn_tutorial (1)
- sklearn tutorial for beginners
handson-ml-master
- Hands-On.Machine.Learning.with.Scikit-Learn.and.TensorFlow
digit-recognition-master
- generateclassifier.py Python脚本来创建文件digits_cls.pkl分类器。 performrecognition.py Python脚本测试分类。 digits_cls.pkl -数字识别的分类器文件。 photo_1.jpg测试图像1号测试分类器 photo_2.jpg测试图像号码2测试分类(generateClassifier.py - Python scr ipt to create the classifier file digits_cl
one-vs-rest
- sklearn中的ovr分类与自己撰写的ovr策略示例(the one-vs-rest strategy)
DT
- 调用于sklearn平台的决策树算法，有着较好的分类能力(The decision tree algorithm used in sklearn platform has good classification ability)
SVM
- 调用于sklearn平台的支持向量机算法，有着较好的分类能力(The support vector machine algorithm for sklearn platform has good classification ability)
K-Nearest Neighbor Classifier
- 调用于sklearn平台的K-Nearest Neighbor Classifier算法，有着较好的分类能力(The k-nearest Neighbor Classifier algorithm for sklearn platform has good classification ability.)
Naive Bayes
- 调用于sklearn平台的朴素贝叶斯算法，有着较好的分类能力(The naive bayes algorithm for sklearn platform is a good classification capability.)
ANN
- 调用于sklearn平台的人工神经网络算法，有着较好的分类能力(The artificial neural network algorithm used in sklearn platform has good classification ability.)
GBDT
- 调用于sklearn平台的梯度提升决策树算法，有着较好的分类能力(The GBDT algorithm used in sklearn platform has good classification ability)
Gauss Bayes
- 使用高斯贝叶斯函数对已有数据进行分类，有样本集(The Gauss Bayes function is used to classify the existing data.)
ml_homework_1
- 此代码使用python中的sklearn实现了对uci中spambase垃圾邮件数据集的分类(This code uses sklearn in Python to realize the classification of spam dataset in UCI)
SMO 算法实现线性 SVM 分类器，对 iris 数据集进行二分类
- 不使用sklearn库，手写实现SMO算法线性 SVM 分类器，对 iris 数据集进行二分类