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Posted to dev@systemml.apache.org by GitBox <gi...@apache.org> on 2020/06/07 16:52:27 UTC
[GitHub] [systemml] t201 opened a new pull request #961: Documentation for Multinomial Logistic Regression using Trust Region method
t201 opened a new pull request #961:
URL: https://github.com/apache/systemml/pull/961
This Documentation is for Multinomial Logistic Regression using Trust Region method.
# Introduction
The DML (Declarative Machine Learning) language has built-in functions which enable access to both low- and high-level function
## `Multinomial Logistic Regression` -Function
The Multinomial Logistic Regression Model is is used to link and provide an estimate of category label probabilities ( y, having multiple rows single column ) which are a response to the numerical vector of explanatory (feature) variables (x, having multiple rows and multiple columns) . The number of links can be 3 or greater unlike binomial regression.
### Usage
```
mulLogisticRegression = function( Matrix X, Matrix Y, intercept_scaling , max_iter , tolerance ,
max1 , maxi2 , verbose )
return(Matrix A)
### Arguments
| Name | Type | Default | Description |
| :---- | :------------- | -------- | :------------------------------- |
| X | Matrix[Double] | -- | Matrix of numerical vector of explanatory variables|
| Y | Matrix[Double] | -- | Matrix of a categorical response variable|
| intercept_scaling| Int | 0 | intercept for shifting and rescaling X columns|
| reg_para | Double | 0 | regularization parameter|
| tolerance | Double | 0.00001 | tolerance ("epsilon")|
| max1 | Int | 100 | max. no. of outer newton interations|
| max2 | Int | 0 | max. no. of inner (conjugate gradient) iterations|
### Returns
| Type | Description |
| :------------- | :---------- |
| Matrix[Double] | probability regression as output |
### Example
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[GitHub] [systemml] asfgit closed pull request #961: Documentation for Multinomial Logistic Regression using Trust Region method
Posted by GitBox <gi...@apache.org>.
asfgit closed pull request #961:
URL: https://github.com/apache/systemml/pull/961
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[GitHub] [systemml] t201 commented on pull request #961: Documentation for Multinomial Logistic Regression using Trust Region method
Posted by GitBox <gi...@apache.org>.
t201 commented on pull request #961:
URL: https://github.com/apache/systemml/pull/961#issuecomment-640247518
Working on forming an example.Some of the things i'm not able to comprehend in the example given for reference-
-cbind
-scaling and shifting of columns to mean 0 and variance 1
-converting Y into indicator matrix
-! converge and ! innerconverge
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[GitHub] [systemml] t201 commented on pull request #961: Documentation for Multinomial Logistic Regression using Trust Region method
Posted by GitBox <gi...@apache.org>.
t201 commented on pull request #961:
URL: https://github.com/apache/systemml/pull/961#issuecomment-640333050
Alright
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[GitHub] [systemml] j143 edited a comment on pull request #961: Documentation for Multinomial Logistic Regression using Trust Region method
Posted by GitBox <gi...@apache.org>.
j143 edited a comment on pull request #961:
URL: https://github.com/apache/systemml/pull/961#issuecomment-643718317
Thank you, @t201 - LGTM. 👍
🎉 🚀
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[GitHub] [systemml] t201 commented on pull request #961: Documentation for Multinomial Logistic Regression using Trust Region method
Posted by GitBox <gi...@apache.org>.
t201 commented on pull request #961:
URL: https://github.com/apache/systemml/pull/961#issuecomment-640477596
Updated the file with example .
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[GitHub] [systemml] j143 commented on pull request #961: Documentation for Multinomial Logistic Regression using Trust Region method
Posted by GitBox <gi...@apache.org>.
j143 commented on pull request #961:
URL: https://github.com/apache/systemml/pull/961#issuecomment-640322731
That's an improvement!
Now, let us work on example.
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[GitHub] [systemml] j143 commented on pull request #961: Documentation for Multinomial Logistic Regression using Trust Region method
Posted by GitBox <gi...@apache.org>.
j143 commented on pull request #961:
URL: https://github.com/apache/systemml/pull/961#issuecomment-643718317
Thank you, @h0901 - LGTM. 👍
🎉 🚀
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