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
Vishal Kapur's Discussions
Passing Nan values to ML Algorithm
Started this discussion. Last reply by Vishal Kapur Sep 19. 3 Replies 1 Like
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…Continue
Input to PCA
Started this discussion. Last reply by Yassin Jomni Sep 8. 5 Replies 1 Like
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…Continue
Feature Selection >> On Labelled Data..
Started this discussion. Last reply by Piyush Agarwal Jan 29. 4 Replies 0 Likes
I am working on a data set that has 10 features, and my label ed output is the 'Weight of humans'.I want to find, out of 10 features, which are he 2 or 3 features due to which the 'Weight' varies..…Continue
Missing Value in DATA - Approaches
Started this discussion. Last reply by Vishal Kapur Nov 4, 2018. 2 Replies 0 Likes
Hello Guys,I am new to ML and I am starting with Exploratory Data Analysis, need help on good material on graphs, statistical methods to use when we first see data, different approach to fill missing…Continue
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"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."
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…"
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…"
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…"
"Thanks.. but just for knowledge, like any algo, such as SVM or linear regression will give error, if I pass nan value.. "
"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…"
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
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
"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??"
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…"
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
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
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