21 Best R Machine Learning Packages in 2020 - The Ultimate Guide
Artificial intelligence is an emerging technology. It is impacting the interactive activities of users through the internet. It possesses the ability to change the way that humans interact, not only with the digital and electronic world but also with each other. It is basically a form of technology with human-like intelligence that can learn, perceive, plan, and process languages. There are two categories of AI, which include “narrow AI” or “general AI.” Narrow AI is domain-specific that we interact with today. By domain-specific, we mean, for instance, language translation. General AI is theoretical and not domain-specific. Just to remain in the context, we would focus only on narrow AI. It is a machine language – a field of computer science for the development of new algorithms and models. Conversely, we can say machine language is an application of Artificial Intelligence. The list of machine languages is vast, with several different categories. Here we are going to discuss R – a programming language in detail. The language falls in the category of Array languages. So, what is R? R comprises many properties. It is a machine language that provides an open-source environment for statistical programming and visualizing. There are many R machine learning packages, which we will be discussing in detail. You can find seamlessly designed AI solutions such as R learning packages in the market by machine learning solution companies such as Folio3.
21 Best R Machine Learning Packages in 2020
The most commonly asked question by potential data scientists is, “What is the best programming language for Machine Learning as a service?” The answer to this question always ends up in a debate about whether to choose R, Python, or MATLAB for Machine Learning. Choosing the programming language for machine learning depends on the requirements of a data problem, the preferences of the data scientist. According to the Kaggle survey, open-source R is the preeminent choice among data specialists who want to understand and explore data by using statistical methods and graphs. There are many R machine learning packages and their advanced implementations for the top machine learning algorithms. Every data specialist must be familiar about them to explore, model, and create a prototype of the given data. Since R is an open-source language, so people can approach it from anywhere in the world. From data collection to reproducible research, you can find a Black Box written by someone else that you can use in your program directly. The Black Box is nothing but a Package in R, which is a collection of pre-written reusable codes. Here are the few basic R learning machine packages:
The CARAT package denotes as Classification and Regression Training. The purpose of this package is to integrate training and model prediction. It allows data specialists to run several different algorithms for a given problem. It also facilitates in investigating the ideal parameters for an algorithm with measured experiments.
CARAT Features And USP: The grid search method of this package explores parameters with the help of a combination of various methods to evaluate the performance of a given model. After having a look at all the trial combinations, the grid search method discovers the combination that gives the best results. CARAT package is among the best machine learning packages in R. After installing the CART package; a developer can run names (getModelInfo()) to see that 217 possible methods require a single package to operate or run. To build any predictive model, CARAT uses train() function; The syntax of train function is train(formula, data, method). CARAT Documentation Download: CARET package is not only for building models, but it also takes care of splitting your data into test and train, alteration, etc.
2) Random Forest
The concept behind its name is to “combine multiple trees to build your forest.” A Random Forest algorithm is the most widely used algorithm in Machine Learning. Its application includes the creation of a large number of decision trees, and then each observation is entered into the decision tree. The common output attained for a maximum of the observations is measured as the final output.
In other words, it takes random samples. Observations are arranged into the decision tree. While using the randomForest algorithm, data specialists have to confirm that the variables must be numeric or factors. Factors cannot have more than 32 levels when applying randomForest. This package allows for solving regression and classification tasks. Training missing values and outliers is one of its many applications. The syntax of this function is: randomForest(formula=, data=)
The name of this package seems a junk value, but this is not the case. It is a very significant package in the R machine learning package. It has very specified functions for implementing Naïve Bayes (conditional probability), SVM, Fourier Transforms, Bagged Clustering, Fuzzy Clustering, etc. E1071 R package implemented the first R interface for SVM. E1071 Documentation Download: The easy way to understand its concept is, let's suppose if a data specialist is trying to find out what is the probability that a person who buys an iPhone 8 also buys an iPhone 8 Case. It is a type of investigation which depends on conditional probability, so data scientists use an e1071 R package that has specialized functions for implementing the Naive Bayes Classifier.
Data scientists use Support Vector Machines (SVM) when they have a dataset that is impossible to separate in the given dimensions, and there is a need to promote that data to higher dimensions to classify or regress it. The syntax for SVM is: SVM(Species ~Sepal.Length + Sepal.Width, data=iris)
Rpart stands for recursive partitioning and regression training. Classification and regression are the two primary purposes of this package. It involves the two-stage procedure. The output model is then represented in the form of binary trees. The general way to plot any function using the Rpart package is to call plot() function. The results might not be appealing by just applying the basic plot() function, so there is a substitute that is the PRP() function. It is an influential and flexible function in rpart.plot package. It is frequently denoted as the authentic Swiss army knife for plotting regression trees.
rpart() function helps to create a relationship between dependent and independent variables so that a business can understand the difference in the dependent variables based on the independent variables. The syntax is: rpart(formula, data=, method=,control=) This formula implies as follows: Data implies the name of the dataset. The method implies the objective. Control implies the system requirement.
KernLab is a package for SVM, kernel feature analysis, ranking algorithm, dot product primitives, Gaussian process, and a spectral clustering algorithm. KernLab’s most common use is for SVM implementations. It comes in use when it is difficult to solve clustering, classification, and regression problems. It has several kernel functions that include tanhdot (hyperbolic tangent kernel Function), polydot (polynomial kernel function), laplacedot (laplacian kernel function) and many more to perform pattern recognition problems. KernLab has its predefined kernels, but the user has the flexibility to create and use their kernel functions.
Data scientists use this package when there is a need to use an artificial neural network (ANN) as it is based on an understanding of how a human brain functions. It is one of the widely used and easy to implement a package of neural networks, but it is restricted to a single layer of nodes. According to several studies, more nodes are not required because they do not contribute to improving the performance of the model but rather increase the calculation time and complexity of the model. This package does not offer any particular set of methods for finding the number of nodes in the hidden layer. Thus, when data specialists apply nnet, it is most likely to recommend that they arrange it in such a way that a value falls between the number of input and output nodes.
The syntax for this package is: nnet(formula, data, size) To view the documentation of its function, go to this link: https://www.rdocumentation.org/packages/nnet/versions/7.3-12/topics/nnet
Dpylr is one of the most popular packages in the field of data science. It provides feasible, fast, and stable functions for data handling. This package contains some set of verbs as functions like mutate(), select(), filter(), and arrange(). The following code is used to install this package: install.packages(“dplyr”) To load this package following syntax is used: library(dplyr)
Ggplot2 is another package for data science. It is the most sophisticated and artistic graphic framework among R packages. The syntax for the installation of this data science package is install.packages(“ggplot2”)
9) Word Cloud
Word cloud, as the name indicates, it consists of thousands of words in a single image. Conversely, we can say that it is a visualization of text data. One great example is speech to text software. It is one of the best machine learning packages in R that creates a representation of words. Data specialists can customize the Worldcloud according to his choice. Like he can place the words randomly in his desired position, or he can place the same kind of words together, etc. In the R machine learning package, two types of libraries are there to make Worldcloud that are Worldcloud and Worldcloud2. Here we will present the syntax of Worldcloud2. The installation syntax for Worldcloud2 is:
Or you can write it directly as: a library(wordcloud2).
It is another popular R package for data science. The function of the tidyr package for data is science is to tidy up the data. It is done by placing variables in the column, observation in the row, and the value in the cell. This package is used to define the standard way of organizing data. For installation syntax, use this code: install.packages(“tidyr”) For loading the package, use this code: library(tidyr)
tm is a machine learning package of R that gives a framework for solving text mining tasks. Text mining is an evolving application of natural language processing these days. Text mining application involves sentiment analysis or news classification. There are several jobs for a developer that he has to perform in this package, such as eliminating unwanted and unrelated words, eliminating punctuation signs and ending words, etc. This package has many adaptable functions to provide ease. Some of them are as follows:
- removeNumbers(): to remove Numbers from the given text document.
- weightTfIdf(): for term Frequency and reverse document frequency.
- tm_reduce(): to combine transformations.
- removePunctuation() to remove punctuation signs from the given text document.
13) MICE Package
MICE package refers to Multivariate Imputation via Chained Sequences. One of the many uses of this package includes imputing the missing values. It is a common problem that developers usually face. Generally, when a developer faces a problem of missing values, he applies basic imputations like replacing with 0, mean or mode, etc. These solutions are not flexible and may result in possible data inconsistency. Therefore MICE package facilitates developers to impute missing values with the help of multiple techniques according to the type of given data. This package consists of various functions like inspecting missing data patterns, diagnosis of the quality of the imputed value, analyses completed dataset, store and export imputed data in different structures, etc. For the package documentation, click on this link https://www.rdocumentation.org/packages/mice/versions/3.6.0/topics/mice
igraph is one of the top machine learning R packages for data science used for network analysis. It is a combination of powerful, professional, accessible, and portable network analysis tools. Moreover, it is an open-source and free package and can be programmed on Python, C/C++, and Mathematica. This package consists of numerous functions that help to produce random and regular graphs, visualization of a graph, etc in computer vision. By using this R package, developers can work on their huge graphs. There are some specific requirements to use this package for Linux. It needs a C and a C++ compiler. The installation syntax for this package is: install.packages(“igraph”) The loading syntax for this package is: library(igraph)
This data science R package helps in visualizing the performance of scoring classifiers. This package is flexible and feasible. It requires only three commands and default values for optional parameters. This package facilitates in developing cutoff-parameterized 2D performance curves. This package comprises of various functions such as: prediction() - used to create prediction objects performance() - used to create performance objects, etc. For package documentation, view this link https://www.rdocumentation.org/packages/ROCR/versions/1.0-7
16) Data Explorer
The DataExplorer package is among the widely used and top machine learning R package for data science. This package serves in the field of exploratory data analysis (EDA), which is one of the predictive analytics tasks. As the name implies, in this package, the data analyst has to be more attentive in data analysis. It is not easy to handle data manually. For this purpose, the DataExplorer package provides automation for data exploration. This package helps in scanning and analyzing every variable to visualize them. It is beneficial for a huge dataset. The data analysis can easily obtain the hidden knowledge of data. You can use this mentioned code to install the package from CPAN: install.packages(“DataExplorer”) To load this package, the following code is needed: library(DataExplorer)
The mlr package of machine learning is one of the most amazing packages. It provides encryption to various machine learning tasks. It can perform several tasks with the help of only a single package rather than to use three packages for three different tasks. mlr package provides coherence for various classification and regression techniques. The techniques involve machine-readable parameter descriptions, clustering, generic re-sampling, filtering, and feature extraction, etc. It can also perform parallel operations. For installation, this code is used: to install.packages(“mlr”) The code for loading this package is; library(mlr)
The arules package arules refers to Mining association rules and Frequent Itemsets. It is also a widely used R machine learning package. This package helps in performing several operations that include representation and transaction analysis of data and patterns and data manipulation. It also provides the C implementations of Apriori and Eclat association mining algorithms.
mboost is another R machine learning package for data science. It is a package that depends majorly on model boosting. It has an operative gradient descent algorithm for enhancing general risk functions. It uses regression trees or component-wise least squares estimates. Also, it provides an interaction model to potentially high-dimensional data
The applications of Party package in R machine learning extends to recursive partitioning. This package imitates the continuous development of collective methods. Party is another package like a randomForest package to make a decision tree, which is based on the Conditional Inference algorithm. The main function of this package is ctree() that reduces the training time and bias. The syntax code for ctree() is: ctree(formula,data)
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Top R Machine Learning Packages Conclusion:
All R machine learning packages are the eminent choice based on their features and functions, and every package best fits according to the given data requirements. There are some default values related to every package in R. Prior to the implementation of an algorithm, a data specialist or a developer must know about its numerous options available. By entering default values will give some outcomes, but that outcome is not likely to be accurate or optimized. In other words, by using the definite functions of R, one can develop an efficient machine learning or data science model. Hence, the R machine learning package is an amazing open-source RStudio tool, providing everyone to avail of the opportunity to use. If you find our blog useful and informative, please share it with your friends and family. If you have any further suggestions or queries, please leave a comment in our comment section. FAQs: Is r used for machine learning and iot as well? Yes, R can be used for both because R itself a machine language applicable in machine learning for data science tasks. As we know that Artificial Intelligence is associated with IoT, and we refer narrow AI to machine learning that includes R. Hence there is a strong relation of R machine learning in IoT applications. Advantages of r packages in machine learning? There are several advantages of R packages in machine learning. Some of them include Open Source – This means everyone has the chance to avail of these packages without the need for license and registration process. Ideal support for data scattering – It allows scattered data to transform in a structured and organized form. Variety of packages – there is a variety of packages for different data issues. Provides quality plotting and graphing – The packages like ggplot2 provides visual tools for plotting and graphing the data. Highly compatible – It is highly compatible and can be combined with many programming languages like C, C++, Python, and Java. Platform independent - It is independent of any platform that means it is cross-platform machine language that can be compatible with Windows, Linux, Mac. Machine learning operations – It supports many machine learning operations like classification, regression, and development of artificial neural networks. Constantly growing – It is progressing continuously with the addition of new features that provide developers to work efficiently without any delay. Which packages in r provides machine learning functionality for beginners? Here is the categorized list of R packages that are useful for beginners:
To manipulate data:
To visualize data:
To model data:
To report results:
Many other R machine learning packages fall in these categories; here, we mentioned the most feasible and popular packages useful for beginners.
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