搜索资源列表
classification
- sklearn库对数据进行分类的机器学习经典例子的python实现-Sklearn library classifying data of machine learning python implementation of a classic example
svm_series
- 用python实现的SVM回归预测的程序，通过Anaconda实现对机器学习包sklearn的调用。-SVM regression using python to achieve predictable procedures, machine learning package sklearn call by Anaconda.
sklearn_random_set_SVM.py
- entropy sampling for svm using sklearn
sklearn-xgboost
- sklearn-xgboost sklearn-xgboost的使用以及创建，这个是学习机器学习时的作业，希望大家指正-sklearn-xgboost sklearn-xgboost use and create, this is the time to learn the job of machine learning, I hope you correct
sklearnExample
- svm分类python练习。sklearn库-svm classification practice python
iris
- 利用机器学习库sklearn库中的k聚类算法进行分类绘图-Machine learning library sklearn library k clustering algorithm to classify and drawing
Adaboost
- Python实现Adaboost算法，数据集为horseColic马疝气病数据集，准确率和sklearn库中的adaboost算法一样。-Python implementation adaboost algorithm, the data set is horseColic horse hernia disease data collection, accuracy and sklearn library adaboost the same algorithm.
DecisionTreeTest
- Sklearn包中决策树的调用，以及算法实现(Call of decision tree in Sklearn package)
自适应推进算法
- 手写的adaboost算法。效率没有sklearn高，不过可以作为学习资料(Handwritten AdaBoost algorithm. The efficiency is not sklearn high, but it can be used as learning material)
g13tsr
- 机器学习启蒙实战学习源码，回归模型，分类模型，聚类和相似度模型，推荐系统，深度学习等学习代码。(Machine learning, practical combat learning source,regression model, classification model, clustering and similarity model, recommendation system, depth learning and other learning code.)
KNN，SVM，决策树，朴素贝叶斯
- 用python的sklearn包分类 简单的对数据进行分类(Sort with Python's sklearn package Simple classification of data)
LinearRegression
- 使用python语言，借助sklearn库实现了多元线性回归的训练和预测(The training and prediction of multiple linear regression are realized by using Python language and sklearn Library)
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)
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)
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.)
sklearn-tree-BN-knn
- 分类器的性能比较与调优: 使用scikit-learn 包中的tree，贝叶斯，knn，对数据进行模型训练，尽量了解其原理及运用。 使用不同分析三种分类器在实验中的性能比较，分析它们的特点。 本实验采用的数据集为house与segment。(Performance comparison and optimization of classifiers: We use tree, Bayesian and KNN in scikit-learnpackage to train the dat
sklearn-SVM
- 支持向量机(SVM)——分类预测，包括核函数调参，不平衡数据问题，特征降维，网格搜索，管道机制，学习曲线，混淆矩阵，AUC曲线等(Support vector machine (SVM) - classification prediction, including kernel function parameter adjustment, unbalanced data problem, feature dimensionality reduction, grid search, pipelin
PythonProject
- 对pcap包中未知网络协议识别与分类，使用的ML库为sklearn(Identification and classification of unknown network protocols in pcap)