Vishal Kapur's Page

Vishal Kapur's discussion was featured

Creating Polynomial Features in ML using sklearn

I have 10 features and all of them are numeric.Does polynomial features only can be used on continuous variables and not on discrete variables?? Out of 10 features which I should pick for creating polynomial features??Choosing criteria..I should take independent features for creating polynomial features or I should take features which are highly correlated with the dependent Y variable?? See More

Dec 26, 2019

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Vincent Granville liked Vishal Kapur's discussion Input to PCA

Oct 8, 2019

Vishal Kapur's discussion was featured

Input to PCA

Suppose I have 20 independent vsariables and I am thinking to go for PCA, Do we need to do the scaling of all these 20 independent variable, or PCA will handle it... And I hope the output of PCA will be scaled features...See More

Oct 1, 2019

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Vishal Kapur replied to Vishal Kapur's discussion Passing Nan values to ML Algorithm

"But NAN values, can't be handled by neural networks, if suppose we have few missing value in one of the features, neural network give NAN as output."

Sep 19, 2019

Yassin Jomni replied to Vishal Kapur's discussion Input to PCA

"The result of the PCA won't be normalized. PCA is an orthogonal linear transformation between your initial data space and a new space that is spanned by the eigenvectors of the covariance/correlation matrix. The only thing you could say is…"

Sep 8, 2019

Yassin Jomni replied to Vishal Kapur's discussion Input to PCA

"It depends. You can compute the PCA on two ways: 1- By computing the eigenvalues and eigenvectors of the covariance matrix: In this case, you need to normalize your data (between 0-1) 2- By computing the eigenvalues and eigenvectors of the…"

Sep 8, 2019

T. Dickinson replied to Vishal Kapur's discussion Input to PCA

"Scaling the input variables is your job as the analyst. Do you want to center and spherize the data? if so, center the data by subtracting the mean vector from all data vectors and sweep out the standard deviations. The result will be a mean vector…"

Sep 3, 2019

Vincent Granville liked Vishal Kapur's discussion Passing Nan values to ML Algorithm

Sep 1, 2019

Vishal Kapur replied to Vishal Kapur's discussion Passing Nan values to ML Algorithm

"Thanks.. but just for knowledge, like any algo, such as SVM or linear regression will give error, if I pass nan value.. "

Aug 29, 2019

Vincent Granville replied to Vishal Kapur's discussion Passing Nan values to ML Algorithm

"NaN stands for "not a number". If it is a missing value instead, some algorithms such as decision trees handle them automatically. See also imputation methods (google the term) to estimate what the value could have been. Now if it is truly…"

Aug 29, 2019

Vishal Kapur's discussion was featured

Passing Nan values to ML Algorithm

Suppose I have 10 independent variable, and I intentionally didn't remove the nan value from few of my independent variable and move it to numpy and then passed it to the ML algo. Will few of the algo give error. Can ML also such as (Decision tree, SVM etc) can handle nan value. If these ML algo doesn't give error, then how will these nan values will be treated/handled internally by the algo.See More

Aug 29, 2019

3 Comments 1 Like

Vishal Kapur posted a discussion

Passing Nan values to ML Algorithm

Suppose I have 10 independent variable, and I intentionally didn't remove the nan value from few of my independent variable and move it to numpy and then passed it to the ML algo. Will few of the algo give error. Can ML also such as (Decision tree, SVM etc) can handle nan value. If these ML algo doesn't give error, then how will these nan values will be treated/handled internally by the algo.See More

Aug 29, 2019

3 Comments 1 Like

Vishal Kapur replied to Vishal Kapur's discussion Input to PCA

"Thanks, but I want to know, do we need to do the scaling of data before PCA, so that all independent variable lies betwee 0 & 1??"

Aug 28, 2019

Jeremy Ireland replied to Vishal Kapur's discussion Input to PCA

"PCA is one approach to it, however, what is your main goal? If you want to reduce variables and group them into indices, PCA is an adequate approach. If you intend on using all 20 IVs, some form of linear/log regression may be best. Is this survey…"

Aug 28, 2019

Vishal Kapur's discussion was featured

Input to PCA

Suppose I have 20 independent vsariables and I am thinking to go for PCA, Do we need to do the scaling of all these 20 independent variable, or PCA will handle it... And I hope the output of PCA will be scaled features...See More

Aug 27, 2019

5 Comments 1 Like

Vishal Kapur posted a discussion

Input to PCA

Suppose I have 20 independent vsariables and I am thinking to go for PCA, Do we need to do the scaling of all these 20 independent variable, or PCA will handle it... And I hope the output of PCA will be scaled features...See More

Aug 27, 2019

5 Comments 1 Like

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Creating Polynomial Features in ML using sklearn

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Creating Polynomial Features in ML using sklearn

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