文章分类 - R
R语言技术学习交流
摘要：The Stan project for statistical computation has a great collection of curated case studies which anybody can contribute to, maybe even me, I was thin
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摘要：When I first started using R there was no such thing as the tidyverse. Although some of the tidyverse packages were available independently, I learned
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摘要：Many introductions to Bayesian analysis use relatively simple didactic examples (e.g. making inference about the probability of success given bernoull
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摘要：In my last posts ([here](http://flovv.github.io/Logo_detection_deep_learning/ and here, I described how one can detect logos in images with R. The fir
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摘要：As promised in my last post, here is a short guide with some tips and tricks for building a documentation website for an R package using pkgdown.In th
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摘要：In this post, I'd like to focus on data munging, e.g. the process of acquiring and arranging data (typically in a tidymanner) prior to data analysis.
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摘要：In the third part in a series on Tidy Time Series Analysis, we’ll use the runCor function from TTRto investigate rolling (dynamic) correlations. We’ll
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摘要：For R users, there hasn’t been a production grade solution for deep learning (sorryMXNET). This post introduces the Keras interface for R and how it c
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摘要：目录： 连接数据库报错：negative length vectors are not allowed 连接数据库报错：first argument is not an open RODBC channel 连接数据库报错：incorrect number of dimensions RStudio
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摘要：概述 data.table包是一个超高性能处理包，在数据处理上代码异常简洁，速度非常快。 由于data.table的语法主要基于[]，有些用法和基础函数会不一致，所以没有放在前面两个专题中一起讲，而是单独拿出来讲。在这个系列里，我会详细说明data.table和基础函的差异，并系统地讲解data.t
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摘要：亚马逊将MXNet指定为官方深度学习平台，1月23日MXNet成为Apache的卵化项目。 无疑，这些将MXNet推向深度学习的热潮中，成为热捧的项目。当然，学习MXNet也是很有必要的。哈哈，加油深度学习。 目前支持以下的语言： Python R C++ Julia Scala Python R
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摘要：Increasing amount of data is available on the web. Web scraping is a technique developed to extract data from web pages automatically and transforming
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Feature Engineering in IoT Age – How to deal with IoT data and create features for machine learning?
摘要：Introduction If you ask any experienced analytics or data science professional, what differentiates a good model from a bad model – chances are that y
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摘要：Introduction Reinforcement learning has recently gained a great deal of traction in studies that call for human-like learning. In settings where an ex
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摘要：Introduction If you have spent some time in machine learning and data science, you would have definitely come across imbalanced class distribution. Th
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摘要：Machine learning uses so called features (i.e. variables or attributes) to generate predictive models. Using a suitable combination of features is ess
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摘要：This week I want to show how to run machine learning applications on a Spark cluster. I am using the sparklyr package, which provides a handy interfac
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摘要：Introduction Over the last 12 months, I have been participating in a number of machine learning hackathons on Analytics Vidhya and Kaggle competitions
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摘要：互动演讲版本请点这里 # dir <- 'F:/Projects/Rpackage/streamlineR' dir <- 'C:/Users/Jianhua/Dropbox/work_doc/Rpackage/streamlineR' knitr::opts_chunk$set(echo = TR
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摘要：分类模型是数据挖掘中应用非常广泛的算法之一，常用的分类算法有Logistic模型、决策树、随机森林、神经网络、Boosting等。针对同一个数据集，可以有这么多的算法进行分析，那如何评估什么样的模型比较合理呢？本文就讲讲常用的模型验证武器，主要包括混淆矩阵、ROC曲线、提升度、增益法和KS统计量。
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