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7 Useful Machine Learning packages in R

Choosing a programming language for machine learning depends on the specifications of a given task and the environment where the ML activities are to be performed. Kaggler’s Favourite Tools survey says that out of 1714 Kaggler’s, 543 are preferring the open source programming language R. R is the best choice for data experts who want to understand and prospect the data, using graphs and statistical methods. It has various advanced implementations and ML packages for the top ML algorithms, that are essential to know for every data scientist to design and explore the given data. The Black Box in R is known as Package, which is a collection of pre-written codes that can be used whenever required. According to CRAN (Comprehensive R Archive Network), approximately 8,341 packages are available today. The users can install these ML packages simply by using the syntax i.e install.packages (“Name_Of_R_Package”). List of a few R packages used for Machine Learning is given below:- RODBC implements ODBC RODBC is the perfect package to choose if you want to convert the data stored in Open Database Connectivity or SQL databases into R data frame. library(RODBC) can be used to load the RODBC function and install.packages(“RODBC”) to install the RODBC package. Wordcloud Package in R Wordcloud allows you to create a graphical picture of words and you can arrange the words in a unsystematic fashion, place the most frequently used words close together in the center, identify the frequency of a specific word etc., resulting in a long lasting impression. It can be loaded with library(wordcloud) and installed using install.packages(“wordcloud”). CARET analyses the best algorithm CARET is one of the leading packages in R language. It is always a question that which is the best algorithm for a given task. This package gives the best results in finding the best algorithm and allows data scientists to run various algorithms for business problems. e1071 helps to find the conditional probability Conditional probability is essential for some forms of analyses, for example: to find the probability that a person who buys a cup also buys a saucer. This can be done with the help of e1071 package. This package of R language also provides naiveBayes( ) function based on conditional probability. Kernlab Package Machine Learning Image processing is one of the difficult tasks, which Support Vector Machine (SVM) eases to a great extent. The implementation of SVM is possible easily with Kernlab package and SVM has several Kernel functions such as laplacedot, polydot, tanhdot and much more. SVM is not at all possible and completely functional without the Kernel functions. RStudio Shiny Package The open source R package i.e Shiny allows beautiful and well-built web frameworks for developing web applications using R. Without the need of any knowledge of HTML, CSS, and JavaScript, it allows you to build interactive web applications. Lubridate Package Machine Learning The techniques used with date-times must be powerful. But in some situations, R language fails to achieve it. According to package authors, the Lubridate package has the same and catchy syntax that makes working with dates and times easy, by providing tools. Conclusion Different machine learning packages are available in the Comprehensive R Archive Network repository. All these packages are used to design efficient models, but make sure that you understand the specifications clearly before applying an algorithm. Because a small change in the parameter can change the output completely.  
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7 Useful Machine Learning packages in R

Susan May
Blog
13th Sep, 2017
7 Useful Machine Learning packages in R

Choosing a programming language for machine learning depends on the specifications of a given task and the environment where the ML activities are to be performed. Kaggler’s Favourite Tools survey says that out of 1714 Kaggler’s, 543 are preferring the open source programming language R. R is the best choice for data experts who want to understand and prospect the data, using graphs and statistical methods. It has various advanced implementations and ML packages for the top ML algorithms, that are essential to know for every data scientist to design and explore the given data.

The Black Box in R is known as Package, which is a collection of pre-written codes that can be used whenever required. According to CRAN (Comprehensive R Archive Network), approximately 8,341 packages are available today. The users can install these ML packages simply by using the syntax i.e install.packages (“Name_Of_R_Package”).

List of a few R packages used for Machine Learning is given below:-

RODBC implements ODBC

RODBC is the perfect package to choose if you want to convert the data stored in Open Database Connectivity or SQL databases into R data frame. library(RODBC) can be used to load the RODBC function and install.packages(“RODBC”) to install the RODBC package.

Wordcloud Package in R

Wordcloud allows you to create a graphical picture of words and you can arrange the words in a unsystematic fashion, place the most frequently used words close together in the center, identify the frequency of a specific word etc., resulting in a long lasting impression. It can be loaded with library(wordcloud) and installed using install.packages(“wordcloud”).

CARET analyses the best algorithm

CARET is one of the leading packages in R language. It is always a question that which is the best algorithm for a given task. This package gives the best results in finding the best algorithm and allows data scientists to run various algorithms for business problems.

e1071 helps to find the conditional probability

Conditional probability is essential for some forms of analyses, for example: to find the probability that a person who buys a cup also buys a saucer. This can be done with the help of e1071 package. This package of R language also provides naiveBayes( ) function based on conditional probability.

Kernlab Package Machine Learning

Image processing is one of the difficult tasks, which Support Vector Machine (SVM) eases to a great extent. The implementation of SVM is possible easily with Kernlab package and SVM has several Kernel functions such as laplacedot, polydot, tanhdot and much more. SVM is not at all possible and completely functional without the Kernel functions.

RStudio Shiny Package

The open source R package i.e Shiny allows beautiful and well-built web frameworks for developing web applications using R. Without the need of any knowledge of HTML, CSS, and JavaScript, it allows you to build interactive web applications.

Lubridate Package Machine Learning

The techniques used with date-times must be powerful. But in some situations, R language fails to achieve it. According to package authors, the Lubridate package has the same and catchy syntax that makes working with dates and times easy, by providing tools.

Conclusion

Different machine learning packages are available in the Comprehensive R Archive Network repository. All these packages are used to design efficient models, but make sure that you understand the specifications clearly before applying an algorithm. Because a small change in the parameter can change the output completely.

 

Susan

Susan May

Blog Author
Susan is a gamer, internet scholar and an entrepreneur. Being a Big Data and Hadoop developer, speaks at international conferences about Big Data as a key enabler of exploring business insights and economics of services.
Website : https://www.zeolearn.com

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