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Could someone kindly explain to me in layman terms, or refer me to any articles, why when doing machine learning (e.g. classification model) sometimes we need to turn continuous numeric feature into nominal?
Also, are there times where doing so is not a good idea...e.g. can cause overfitting...or?
Thanks
Wes
This article has a nice explanation
8 Ways to deal with Continuous Variables in Predictive Modeling
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For a course term project we have to build a machine learning algorithm in which user fills the form and the algorithm analyse the best suitable university based on the responses. I am new in the field of machine learning and I do not know what kind of algorithm can we use. Is the recommendation systems a right approach for this?
I did some review on the internet for some similar projects, however still can not find a good resource.
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I am looking for the machine learning correct approach for predicting the lottery numbers, not the most accurate answer but at least we have some predicted output. I am implementing the regression based and neural network models for this. Is their any specific approach which follows this?
It is impossible. The lottery numbers are random - actually to be more specific, the system is chaotic. You would require the initial configuration (positions etc) to insane (possibly infinite) precision to be able to make any predictions. Basically, don't even try it.
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i am stuck with this problem. I have generated handwritten digit using GANs. And now I want to evaluate how accurate my generated handwritten digits are. Is there any way??
The most common ways to evaluate the network is to either show the visual output as in the original gan paper (http://papers.nips.cc/paper/5423-generative-adversarial-nets) or by showing on how well they do for semi-supervised learning (http://papers.nips.cc/paper/6125-improved-techniques-for-training-gans.pdf). This means using GANs to improve classification performance for the case, where only little data is available .
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I would like to interpret problem on picture below, which is about perceptron learning. It is about supervised learning wiht a training set, so correctness of values should be checked against a predefined set of values. I don't exactly know, how A, B and bias(b) values come. Could you please explain meaning of these and how these computed and changed during the learning process?
Here you have an intuitive, visual, interactive and beautiful guide to the basics concepts of neural networks:
https://jalammar.github.io/visual-interactive-guide-basics-neural-networks/
It will problably solve all your doubts. However, if you still have more questions after the reading, you will be able to ask something more specific. Enjoy!
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I am bit confused when people talk about text mining and topic modelling randomly in the discussions. Can anyone please explain the exact meaning of these two and their differences.
Text mining is a broad topic.
Topic modeling is one possible subtopic of text mining.
For further details, please see Wikipedia.