Hi,
I'm going to test and compare my model with other model such as Neural network and Random Forest.
Could anyone recommend me any function/package for random forest and neural network or share any related notes/ sample code in Matlab to impute missing values?
I found few documentations for neural network, but unluckily i did not find any appropriate function to deal with missing data:
https://uk.mathworks.com/help/pdf_doc/nnet/index.html?s_cid=doc_ftr
If someone could share anything, it would help me expedite my research progress.
Thank you
Dear Aisyah mat jasin,
I have some books written by Chinese scholar about Neural Network based on Matlab, however they are all written in Chinese. But I do think the English book focusing on your topic must be existing.
See more
https://jp.mathworks.com/support/books/book106283.html
fillmissing function in MATLAB with several options can be used to fill missing entries of an array or table with a constant value .
'previous' — previous non-missing value
'next' — next non-missing value
'nearest' — nearest non-missing value
'linear' — linear interpolation of neighboring, non-missing values (numeric, duration, and datetime data types only)
'spline' — piecewise cubic spline interpolation (numeric, duration, and datetime data types only)
'pchip' — shape-preserving piecewise cubic spline interpolation (numeric, duration, and datetime data types only)
Hello,
I think before looking for the MATLAB code for missing value imputation, you need to identify the ways we can impute missing values. Obviously the simplest technique is to just remove the entire sample from the data, however, that is not very productive, as in ML we like to have more samples to train. Now, as far as I know, there isn't any Matlab package that contains the various imputation techniques where we just use the appropriate function (last checked today). I used the R package called MICE which helps you impute missing values with plausible data values. These plausible values are drawn from a distribution specifically designed for each missing data point. This package can be obtained from:
https://www.r-bloggers.com/imputing-missing-data-with-r-mice-package/
In the past, I also used SPSS to impute missing data. An excellent tutorial with examples is found in this link:
https://www.uvm.edu/~dhowell/StatPages/Missing_Data/MissingDataSPSS.html
Also remember, once you impute missing values, you'd ideally want to measure the performance of these imputation techniques. One commonly applied method is the root-mean-squared-method and you can use it to determine which technique gives you the least RMSE.
Now once you figure out which techniques you want to try, then let me know what you decided to use, and we can help you proceed accordingly!
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