machine-learning-algorithms | code for some machine learning algorithms | Machine Learning library

by algorithmdog Python Version: Current License: No License

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kandi X-RAY | machine-learning-algorithms Summary

machine-learning-algorithms is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Example Codes applications. machine-learning-algorithms has no bugs, it has no vulnerabilities and it has low support. However machine-learning-algorithms build file is not available. You can download it from GitHub.

code for some machine learning algorithms

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machine-learning-algorithms has a low active ecosystem.

It has 5 star(s) with 6 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

machine-learning-algorithms has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of machine-learning-algorithms is current.

Quality

machine-learning-algorithms has no bugs reported.

Security

machine-learning-algorithms has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

machine-learning-algorithms does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

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machine-learning-algorithms releases are not available. You will need to build from source code and install.

machine-learning-algorithms has no build file. You will be need to create the build yourself to build the component from source.

Top functions reviewed by kandi - BETA

kandi has reviewed machine-learning-algorithms and discovered the below as its top functions. This is intended to give you an instant insight into machine-learning-algorithms implemented functionality, and help decide if they suit your requirements.

Generate visualization data .
Performs one step of a step .
Generate random samples .
Evaluate an E - step equation
Minimized GMM algorithm
Initialize the polynomial .
Calculate the log - likelihood of a given model .
Normal distribution .
r Normal distribution .

Get all kandi verified functions for this library.

machine-learning-algorithms Key Features

No Key Features are available at this moment for machine-learning-algorithms.

machine-learning-algorithms Examples and Code Snippets

No Code Snippets are available at this moment for machine-learning-algorithms.

Community Discussions

Trending Discussions on machine-learning-algorithms

How to calculate 95% CI for accuracy and kappa in caret

unable to import cross_validation

Using ROC AUC score with Logistic Regression and Iris Dataset

Accuracy SD not showing up in R

Error in impute() in R

QUESTION

How to calculate 95% CI for accuracy and kappa in caret

Asked 2021-Mar-05 at 10:44

I am running k-fold repeated training with the caret package and would like to calculate the confidence interval for my accuracy metrics. This tutorial prints a caret training object that shows accuracy/kappa metrics and associated SD: https://machinelearningmastery.com/tune-machine-learning-algorithms-in-r/. However, when I do this, all that is listed are the metric average values.

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ANSWER

Answered 2021-Mar-01 at 07:44

It looks like it is stored in the results variable of the resultant object.

Source https://stackoverflow.com/questions/66416014

QUESTION

unable to import cross_validation

Asked 2019-Oct-27 at 08:40

While building a new neural network I seem unable to split the data. For some unknown reason it wont import train.test.split

ImportError: cannot import name 'cross_validation'

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ANSWER

Answered 2018-Oct-11 at 09:23

The module has been removed since 0.20.

Deprecated since version 0.18: This module will be removed in 0.20. Use sklearn.model_selection.cross_val_score instead.

Source https://stackoverflow.com/questions/52756324

QUESTION

Using ROC AUC score with Logistic Regression and Iris Dataset

Asked 2019-May-03 at 11:17

What I need is to:

Apply a logistic regression classifier
Report the per-class ROC using the AUC.
Use the estimated probabilities of the logistic regression to guide the construction of the ROC.
5fold cross validation for the training your model.

For this, my approach was to use this really nice tutorial:

From his idea and method I simply changed how I obtain the raw data which I am getting like this:

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ANSWER

Answered 2019-May-03 at 11:17

The iris dataset is usually ordered with respect to classes. Hence, when you split without shuffling, the test dataset might get only one class.

One simple solution would be using shuffle parameter.

Source https://stackoverflow.com/questions/55944240

QUESTION

Accuracy SD not showing up in R

Asked 2019-Jan-29 at 12:07

I tried to follow the example codes at https://machinelearningmastery.com/tune-machine-learning-algorithms-in-r/ but my output did not showing up accuracy and kappa sd. What am i missing? My caret library is 3.5.2 on Windows 10 Pro.

My output was:

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ANSWER

Answered 2019-Jan-29 at 12:06

In the tutorial it's not specified how the output with SD's was obtained. It actually wasn't just rf_default. Instead,

Source https://stackoverflow.com/questions/54418798

QUESTION

Error in impute() in R

Asked 2017-Sep-14 at 15:19

I'm learning Random Forest. For learning purpose I'm using following link random Forest. I'm trying to run the code given in this link using my R-3.4.1. But while running the following code for missing value treatment

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ANSWER

Answered 2017-Sep-14 at 15:06

The key mistake (among many mistakes) in that code was that there is no data parameter. The parameter name is obj. When I change that the example code runs.

You also need to set on= or setkey given that the object is a data.table, or simply change it to a data.frame for the imputation step:

Source https://stackoverflow.com/questions/46222304

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

Vulnerabilities

No vulnerabilities reported

Install machine-learning-algorithms

You can download it from GitHub.
You can use machine-learning-algorithms like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

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 .

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