Intel® oneAPI Data Analytics Library
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QR Decomposition - Linear Regression linear dependency


I'm working with linear regression based on QR decomposition algorithm and for some  datasets,  the Mean Squared Error (MSE)  obtained is very high. I realized that, in this situations, the matrix rank isn't full because there are rows/columns linearly dependent.  

When I execute the same datasets in algorithm based on Normal Equation Regularized by ridge method, the MSE generated is the expected. 

How proceed in this situations? It's possible find a linear dependency relation among attributes using DAAL? 

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Hi Andre,

You can analyze the dependence between attributes of the dataset using Intel DAAL correlation algorithm. The value ~ +/-1 in (i,j) position of the correlation matrix  would indicate linear dependence between i and j attributes. Let me know, if you need any help on use of the algorithm.

To better understand your use scenario of the linear regression, can you please provide the additional details:

- what is the typical size of the input dataset used to train linear regression model?

- are you interested in sparse or dense version of the linear regression (with or without regularization)?

- do you train the model for one or several dependent variables/responses?

- do you use publically available datasets for testing of the linear regression? If so, can you share the links with us?




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