I'm writing a paper about Data Augmentation and I'm looking for some way of increasing the size of a dataset. I'm already aware of the techniques used for images (transformations, PCA, blurs, etc.) but I'm having trouble finding augmentation techniques for classic numeric datasets such as Iris for example.  I already explored some obvious methods which are the following :   Uniform Random Generation : This really naive method consists of creating a new instance based on the min an max of the existing ones, the value of each feature is generated randomly with a uniform probability. (The mins and maxs are calculated from the values of the concerned feature of the concerned class each time) Normal Random Generation : Same as Uniform but the probability function is now a gaussian curve. Which is of course less naive since generated value fit the initial data distribution. Adding Noise : This method is a little different since it consist of cloning initial values, but each time adding some noise to it. This methods aims to strengthen the models and prevent overfitting.   I'm kinda new to ML and data augmentation and these 3 methods can look pretty obvious, i was wondering if anyone had an idea of different methods/algorithms to do such a task.  Thanks for your advices.  DP  NB: Text Augmentation is out of my project scope but if you have any info on that, please share !
In many machine learning applications, the so called data augmentation methods have allowed building better models. For example, assume a training set of $100$ images of cats and dogs. By rotating, mirroring, adjusting contrast, etc. it is possible to generate additional images from the original ones.  In the case of images, the data augmentation is relatively straightforward. However, suppose (for example) that one has a training set of $100$ samples and few hundred continuous variables that represent different things. The data augmentation does not anymore seem so intuitive. What could be done in such case?
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How to Query all opportunities ids from account I write a sample query to fetch account Ids from cases of a particular record type.Then i wrote another sample query parent to child relationship to fetch opportunity ids from accounts.  My sample code  List&lt;Case&gt; c=new List&lt;Case&gt;(); c=[Select Id,accountId from case where RecordTypeId = '01290000000sF3L']; List&lt;Id&gt; accountIds= new List&lt;Id&gt;(); List&lt;Account&gt; accounts=new List&lt;Account&gt;(); List&lt;Opportunity&gt; opps=new List&lt;Opportunity&gt;(); for(Case cl:c)  { accountIds.add(cl.accountId); } accounts=[Select Id,name,(select Id,name from opportunities) from account        where Id in:accountIds]; System.debug('Data in accounts '+accounts);   But my issue is how to get opportunity id from above Soql accounts means how to add those opportunity id's to the list.  for(Account ao:accounts) { //How to fetch Opportunity ids from above account list. } Prove the $n$th Fibonacci number is the integer closest to $\frac{1}{\sqrt{5}}\left( \frac{1 + \sqrt{5}}{2} \right)^n$ Prove that the $n$th Fibonacci number $f_n$ is the integer that is closest to the number $$\frac{1}{\sqrt{5}}\left( \frac{1 + \sqrt{5}}{2} \right)^n.$$   Hi everyone, I don't really understand the problem. 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