uuddoop-CSDN博客

原创机器学习算法概述

目录机器学习概述01 监督学习02 无监督学习：03 半监督学习04 强化学习机器学习算法介绍1 回归算法01线性回归：02非线性回归03逻辑回归2 聚类01基于层次的聚类02基于分割（划分）的聚类03基于密度的聚类04基于网格的聚类05基于模型的聚类3分类：01 逻辑回归02 朴素贝叶斯分类器03 SVM算法：04 KNN05 决策树：06 随机森林4降维有监督降维无监督降维5 时间序列：常用算法：AR、MA、ARMA、ARIMA、SARIMAProphet模型LSTM6深度学习：01神经网络：02反向传

2023-04-07 14:33:44 616 1

原创 python3 导出DataFrame到本地excel（数据条数大于65535）

如果数据行数 < 65535,可以直接使用to_excel导出，DFname.to_excel(excel_contents)其中，DFname 为DataFrame文件名，excel_contents为excel储存路径，如：’d:test.xlsx。如果数据行数 > 65536,无法使用to_excel，可以使用以下方法：from openpyxl.workbook import Workbookdef DF2xls(filename,xlsname): #DataF

2021-01-14 09:38:54 2360 2

原创 Python3 读取odps数据库数据

Python3 读取odps数据库数据pyodps安装如果未安装pyodps包，则需要先安装：pip install pyodpsODPS常用包导入from odps import ODPSfrom odps import DataFrameimport pandas as pdfrom collections import defaultdict连接odps数据库o = ODPS(access_id='access id', #登陆账号 secret_access_

2021-01-05 16:27:04 5344

原创 python3实现决策树可视化(graphviz)中文乱码

python3实现决策树可视化(graphviz)中文乱码python3实现决策树可视化需要用到包graphviz，graphviz默认不支持中文，所以当dot文件中包含中文字符时会出现乱码。**解决乱码方法一：**将dot源文件保存为UTF8（Ubuntu下默认为UTF8，Windows下默认为ASNI）格式，并将dot文档中的所有中文都进行修改，把中文包含在英文的引号（“”）中，如下：“中文字符”但是对于生成决策树dot文本中中文较多时，手动修改需要耗费时间较长，可行性较差，则可以用第二种

2020-11-20 11:28:51 2233 1

原创 Python3 读取csv文件

方法一,panda包读取：没有安装panda包需要先安装panda包：pip install pandas读取csv数据：import pandas as pdcased = pd.read_csv('d:/dataname.csv')若出现错误：UnicodeDecodeError: 'utf-8' codec can't decode byte 0xc8 in position 2: invalid continuation byte通过修改encoding值进行修改，如下所示：c

2020-11-19 10:30:48 1135

原创 Graphviz2.44.2 安装教程

**Graphviz2.44.2 安装教程**Graphviz 是一款开源的可视化图形工具，用Graphviz来绘图时，只需要编写dot脚本，就能够对图中的节点完成自动布局，可以很方便的用来绘制结构化的图形网络，支持多种格式输出。本文安装Graphviz2.44.2，适用于Windows 64x系统一、下载地址： https://graphviz.org/download/选择适用windows的安装包：二、安装：下载好，直接双击进行安装：具体安装过程如下（需要修改path路径）：

2020-11-18 13:37:06 480

Spotting Outliers in Large Distributed Datasets using

ABSTRACT Outliers are abnormal instances or observations. Detecting data outliers is a very important concept in Knowledge data discovery. Outlier detection has been studied in the context of a large number of research areas like large distributed systems, data mining, wireless sensor networks(WSN), health monitoring, environmental science, statistics, etc., Density based (DB) outlier detection techniques are robust in detecting outliers. In many applications, too much voluminous distributed data is generating every day. Finding deviating observations in the large distributed database rather than in any individual database is not a simple task. Integrating distributed database cause two major problems. First, render massive data from different databases. In addition, data integration may cause violation of data security and leakage of sensitive information. In this work we propose cell density based mechanism for outlier detection (CDOD) in large distributed databases. A centralized detection paradigm is used; it allows overcoming the expensive data integration and information leakage. The experimental results show robustness for finding outliers in large number of databases, instances and attributes

2018-06-05

online clustering of bandits

Abstract We introduce a novel algorithmic approach to content recommendation based on adaptive clustering of exploration-exploitation("bandit") strategies.We provide sharo regret analysis of this algorithm in a standard stochastic noise setting,demonstrate its scalability properties,and ……