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is it possible to use logistic regression to identify prime numbers?
i´m trying to project a system with supervised logistic regression with a predefined database numbers and it´s classification (1 = Prime, 0 = Not Prime), using this data i want the computer to use this type of alghorythm to identify other numbers that aren´t classified on DB,
is it possible, or i´m trying to do something impossible?
Given the right network configuration and enough time, I don't know why it would be impossible.
It seems others have had success with different models and you might get a better idea from them:
Early success on prime number testing via artificial networks is presented in A Compositional Neural-network Solution to Prime-number Testing, László Egri, Thomas R. Shultz, 2006. The knowledge-based cascade-correlation (KBCC) network approach showed the most promise, although the practicality of this approach is eclipsed by other prime detection algorithms that usually begin by checking the least significant bit, immediately reducing the search by half, and then searching based other theorems and heuristics up to 𝑓𝑙𝑜𝑜𝑟(𝑥‾‾√). However the work was continued with Knowledge Based Learning with KBCC, Shultz et. al. 2006.
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I am using support vector regressor. I want to predict personality as shown in screenshot! Is it possible to predict when y is in string format? I used onehot encoder but its not working.
This is not a regression task, but classification. 'not working' is not very informative, however normally you'd just map classes to integers. Either sklearn.preprocessing.LabelEncoder, sklearn.preprocessing.label_binarize().argmax(axis=1), pandas.factorize() or manual mapping should get the job done.
Worth noting support vector machines don't handle multiclass problems natively, so you may encounter troubles depending on the exact model you use. At least the latest sklearn versions should handle it automatically when using models like sklearn.svm.LinearSVC, building N binary classifiers under the hood.
I'd also recommend getting acquainted with a more elegant way of ensembling SVMs for multiclass problems, using sklearn.multiclass.OutputCodeClassifier().
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I already have 85 accuracy on my sklearn text classifier. What are the advantages and disadvantages of making a rule based system? Can save doing double the work? Maybe you can provide me with sources and evidence for each side, so that I can make the decision baed on my cirucumstances. Again, I want to know when ruls-based approach is favorable versus when a ML based approach is favorable? Thanks!
Here is an idea:
Instead of going one way or another, you can set up a hybrid model. Look at typical errors your machine learning classifier makes, and see if you can come up with a set of rules that capture those errors. Then run these rules on your input, and if they applied, finish there; if not, pass the input on to the classifier.
In the past I did this with a probabilistic part-of-speech tagger. It's difficult to tune a probabilistic model, but it's easy to add a few pre- or post-processing rules to capture some consistent errors.
https://www.linkedin.com/feed/update/urn:li:activity:6674229787218776064?commentUrn=urn%3Ali%3Acomment%3A%28activity%3A6674229787218776064%2C6674239716663156736%29
Yoel Krupnik (CTO & co-founder | smrt - AI For Accounting) writes:
I think it really depends on the specific problem. Some problems can be completely solved with rule based logic, some require machine learning (often in combination with rule based logic before or after).
Advantages of the rule based are that it doesn't require labeled training data, might quickly provide decent results used as a benchmark and helps you better understand the problem for future labeling / text manipulations required by the ML algorithm.
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Why does a small dataset have a high variance? Our professor once said it. I just did not understand it. Any help would be greatly appreciated.
Thanks in advance.
if your data set is small and you train your model to fit the data set ,it is easy to have overfitting problems.If your data set is big enough,a little overfitting may not a big problem ,but not in a small data set.
Every single one of us, by the time we are entering our professional careers, have been exposed to a larger visual dataset then the largest dataset available for AI researchers. On top of this, we have sound, smell, touch, and taste data all coming in from our external senses. In summary, humans have a lot of context on the human world. We have a general common-sense understanding of human situations. When analyzing a dataset, we combine the data itself with our past knowledge in order to come up with an analysis.
The typical machine learning algorithm has none of that — it has only the data you show to it, and that data must be in a standardized format. If a pattern isn’t present in the data, there is no way for the algorithm to learn it. That's why when given a small dataset it is more prone to error.
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(homework problem)
Which of the following problems are best suited for the learning approach?
Classifying numbers into primes and non-primes.
Detecting potential fraud in credit card charges.
Determining the time it would take a falling object to hit the ground.
Determining the optimal cycle for trafic lights in a busy intersection
I'm trying to answer your question without doing your homework.
Basically you can think of machine learning as a way to extract patterns from data where all other approaches fail.
So first clue here: If there is an analytic way to solve the problem then don't use machine learning! The analytic algorithm will likely be faster, more efficient, and 100% correct.
Second clue is: There has to be a pattern in the data. If you as a human see a pattern, machine learning can find it too. If lots of smart humans who are experts of the respective domain don't see a pattern then machine learning will most likely fail. Chaos can not be learned, i.e. classified/predicted.
That should answer your question. Make sure to also read the summary on wikipedia to get an idea whether a problem can be solved using supervised, unsupervised, or reinforcement learning.
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Hopefully the last NN question you'll get from me this weekend, but here goes :)
Is there a way to handle an input that you "don't always know"... so it doesn't affect the weightings somehow?
Soo... if I ask someone if they are male or female and they would not like to answer, is there a way to disregard this input? Perhaps by placing it squarely in the centre? (assuming 1,0 inputs at 0.5?)
Thanks
You probably know this or suspect it, but there's no statistical basis for guessing or supplying the missing values by averaging over the range of possible values, etc.
For NN in particular, there are quite a few techniques avaialble. The technique i use--that i've coded--is one of the simpler techniques, but it has a solid statistical basis and it's still used today. The academic paper that describes it here.
The theory that underlies this technique is weighted integration over the incomlete data. In practice, no integrals are evaluated, instead they are approximated by closed-form solutions of Gaussian Basis Function networks. As you'll see in the paper (which is a step-by-step explanation, it's simple to implement in your backprop algorithm.
Neural networks are fairly resistant to noise - that's one of their big advantages. You may want to try putting inputs at (-1.0,1.0) instead, with 0 as the non-input input, though. That way the input to the weights from that neuron is 0.0, meaning that no learning will occur there.
Probably the best book I've ever had the misfortune of not finishing (yet!) is Neural Networks and Learning Machines by Simon S. Haykin. In it, he talks about all kinds of issues, including the way you should distribute your inputs/training set for the best training, etc. It's a really great book!