Machine-Learning-in-R | decision trees , random forest | Machine Learning library

by dlab-berkeley CSS Version: Current License: Non-SPDX

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | Machine-Learning-in-R Summary

Machine-Learning-in-R is a CSS library typically used in Artificial Intelligence, Machine Learning applications. Machine-Learning-in-R has no bugs, it has no vulnerabilities and it has low support. However Machine-Learning-in-R has a Non-SPDX License. You can download it from GitHub.

This is the repository for D-Lab’s Introduction to Machine Learning in R workshop. View the associated slides here.

Support

Quality

Security

License

Reuse

Support

Machine-Learning-in-R has a low active ecosystem.

It has 177 star(s) with 70 fork(s). There are 18 watchers for this library.

It had no major release in the last 6 months.

There are 2 open issues and 11 have been closed. On average issues are closed in 359 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Machine-Learning-in-R is current.

Quality

Machine-Learning-in-R has no bugs reported.

Security

Machine-Learning-in-R has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

Machine-Learning-in-R has a Non-SPDX License.

Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

Reuse

Machine-Learning-in-R releases are not available. You will need to build from source code and install.

Installation instructions are available. Examples and code snippets are not available.

Top functions reviewed by kandi - BETA

kandi's functional review helps you automatically verify the functionalities of the libraries and avoid rework.
Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of Machine-Learning-in-R

Get all kandi verified functions for this library.

Machine-Learning-in-R Key Features

No Key Features are available at this moment for Machine-Learning-in-R.

Machine-Learning-in-R Examples and Code Snippets

No Code Snippets are available at this moment for Machine-Learning-in-R.

Community Discussions

Trending Discussions on Machine-Learning-in-R

Clarity regarding the use of sample() function in R to set up training and test sets for ML.

Machine Learning: predicting a new value in R

QUESTION

Clarity regarding the use of sample() function in R to set up training and test sets for ML.

Asked 2018-Aug-05 at 09:47

I am trying to understand this example of the KNN algorithm in R by Datacamp: Machine Learning in R for beginners

I am having trouble understanding how they execute sampling to set up the training and test data sets.

I am able to follow the code till this line:

...

ANSWER

Answered 2018-Aug-05 at 09:47

My question is how are the variable ind and the iris data set related.

They're not, but they needn't be. For example, there is no intrinsic relationship between the numbers 1-5 and the iris dataset, yet

Source https://stackoverflow.com/questions/51688534

QUESTION

Machine Learning: predicting a new value in R

Asked 2018-Apr-22 at 11:20

Just follow a ML tutorial (https://machinelearningmastery.com/machine-learning-in-r-step-by-step/). I'm trying to predict a single output on a single vector.

I'm using the IRIS dataset, and trying to predict with an LDA Algorythm. I'm trying to use my nval vector (c(4.3,3.1,1.5,0.1) to predict a single outcome, what Species is it?

...

ANSWER

Answered 2018-Apr-22 at 11:20

library(caret)
control <- trainControl(method="cv", number=10)
metric <- "Accuracy"

validation_index <- createDataPartition(iris$Species, p=0.80, list=FALSE)
validation <- iris[-validation_index,]
dataset <- iris[validation_index,]  

fit.lda <- train(Species~., data=dataset, method="lda", metric=metric, 
                 trControl=control)

Source https://stackoverflow.com/questions/49965230

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install Machine-Learning-in-R

Please follow the notes in participant-instructions.md. The seven algorithm R Markdown files (lasso, decision tree, random forest, xgboost, SuperLearner, PCA, and clustering) are designed to function in a standalone manner.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

Find more information at: