I am trying to implement an SVM in Rapidminer. However I am presented with several SVM implementations, libsvm, mysvm,JMySVM, Particle Swarm Optimization based SVM and Evolutionary SVM. Know I know the basic differences between the implementations but what are the advantages and disadvantages of them to know which one to implement?
I am not finding much information about this online, I would like to avoid a try them all to see which one presents the best results. So I would like to know in which situation I should use them.
From the first, you seem to confuse different implementations and algorithms. As far as I know, libsvm, mysvm and JmySVM are standard implementation which solve the SVM optimization problem by algorithms such as sequential minimal optimization.
On the contrary, the other SVMs you mentioned (additionally) use less common approaches like particle swarm optimization or evolutionary algorithms for the optimization. Such methods usually give you good approximation with small effort, which might be advantageous for large-scale problems (--but I admit I don't know the exact motivation for their invention).
If you are looking for the SVM model which is common in machine learning and related fields, I would suggest you to try the library libsvm. Alternatively, you can have a look on the collection here.
Related
I am working on a machine learning project and my data is imbalanced, with one class having a much smaller number of examples than the other. This has led to biased models and poor predictive performance, especially for my target variable which is of high importance. I have already tried resampling techniques such as oversampling, undersampling, and both, but I'm still struggling to achieve the desired performance.
One possible solution from paulduf for handling class imbalance is to use a package that is specifically designed for this purpose. They mentioned using the 'imbalanced-learn' package, which is compatible with scikit-learn, has a lot of stars and looks up-to-date/maintained. I looked at the package and it does provide a variety of methods for dealing with imbalanced datasets, such as sampling techniques, cost-sensitive learning, and ensemble methods. Here's the link to the package: github.com/scikit-learn-contrib/imbalanced-learn imbalanced-learn.org/stable/user_guide.html Thank you paulduf.
Aside from using a package, what are some other effective tips and best practices for handling class imbalance in machine learning that have been shown to work in real-world scenarios? Are there any specific methods that have been proven to be successful in addressing this problem, such as anomaly detection techniques? Any help or guidance would be greatly appreciated, as I am looking to improve the performance of my machine learning models in the context of imbalanced datasets.
Is anyone aware whether someone has produced a cheatsheet--preferably like a summary table--of various machine learning techniques (e.g. kNN, regression tree, Naive Bayes, linear regression, neural nets, etc.) along with the type of dependent and independent variables they accept (continuous, categorical, binary, etc.)?
I realize there can be a lot of shifty grey area here, but a general guide of some sort could be helpful for becoming familiar with these tools. I've done a lot googling not turned up anything like this yet.
Cheers
Check out http://ml-cheatsheet.readthedocs.io/en/latest/
It covers basic regression as well as popular neural net architectures.
Also check out this compact infographic: https://blogs.sas.com/content/subconsciousmusings/2017/04/12/machine-learning-algorithm-use/
A nice one as well: https://learn.microsoft.com/en-us/azure/machine-learning/studio/algorithm-cheat-sheet
As far as I know, NEAT (NeuroEvolution of Augmenting Topologies) is an algorithm that uses the concept of evolution to train a neural network. On the other hand, reinforcement learning is a type of machine learning with the concept of "rewarding" more successful nodes.
What is the difference between these two fields as they seem to be quite similar? Or is NEAT derived from reinforcement learning?
In short they have barely anything in common.
NEAT is an evolutionary method. This is a black box approach to optimization of functions. In this case - performance of the neural net (which can be easily measured) wrt. to its architecture (which you alter during evolution).
Reinforcement learning is about agents, learning policies to behave well in the environment. Thus they solve different, more complex problem. In theory you could learn NEAT using RL, as you might pose the problem of "given a neural network as a state, learn how to modify it over time to get better performance". The crucial difference will be - NEAT output is a network, RL output is a policy, strategy, algorithm. Something that can be used multiple times to work in some environment, take actions and obtain rewards.
Many machine learning competitions are held in Kaggle where a training set and a set of features and a test set is given whose output label is to be decided based by utilizing a training set.
It is pretty clear that here supervised learning algorithms like decision tree, SVM etc. are applicable. My question is, how should I start to approach such problems, I mean whether to start with decision tree or SVM or some other algorithm or is there is any other approach i.e. how will I decide?
So, I had never heard of Kaggle until reading your post--thank you so much, it looks awesome. Upon exploring their site, I found a portion that will guide you well. On the competitions page (click all competitions), you see Digit Recognizer and Facial Keypoints Detection, both of which are competitions, but are there for educational purposes, tutorials are provided (tutorial isn't available for the facial keypoints detection yet, as the competition is in its infancy. In addition to the general forums, competitions have forums also, which I imagine is very helpful.
If you're interesting in the mathematical foundations of machine learning, and are relatively new to it, may I suggest Bayesian Reasoning and Machine Learning. It's no cakewalk, but it's much friendlier than its counterparts, without a loss of rigor.
EDIT:
I found the tutorials page on Kaggle, which seems to be a summary of all of their tutorials. Additionally, scikit-learn, a python library, offers a ton of descriptions/explanations of machine learning algorithms.
This cheatsheet http://peekaboo-vision.blogspot.pt/2013/01/machine-learning-cheat-sheet-for-scikit.html is a good starting point. In my experience using several algorithms at the same time can often give better results, eg logistic regression and svm where the results of each one have a predefined weight. And test, test, test ;)
There is No Free Lunch in data mining. You won't know which methods work best until you try lots of them.
That being said, there is also a trade-off between understandability and accuracy in data mining. Decision Trees and KNN tend to be understandable, but less accurate than SVM or Random Forests. Kaggle looks for high accuracy over understandability.
It also depends on the number of attributes. Some learners can handle many attributes, like SVM, whereas others are slow with many attributes, like neural nets.
You can shrink the number of attributes by using PCA, which has helped in several Kaggle competitions.
I am working on testing several Machine Learning algorithm implementations, checking whether they can work as efficient as described in the papers and making sure they could offer a great power to our statistic NLP (Natural Language Processing) platform.
Could u guys show me some methods for testing an algorithm implementation?
1)What aspects?
2)How?
3)Do I have to follow some basic steps?
4)Do I have to consider diversity specific situations when using different programming languages?
5)Do I have to understand the algorithm? I mean, does it offer any help if I really know what the algorithm is and how it works?
Basically, we r using C or C++ to implement the algorithm and our working env is Linux/Unix. Our testing methods only focus on black box testing and testing input/output of functions. I am eager to improve them but I dont have any better idea now...
Great Thx!! LOL
For many machine learning and statistical classification tasks, the standard metric for measuring quality is Precision and Recall. Most published algorithms will make some kind of claim about these metrics, or you could implement them and run these tests yourself. This should provide a good indicative measure of the quality you can expect.
When you talk about efficiency of an algorithm, this is usually some statement about the time or space performance of an algorithm in terms of the size or complexity of its input (often expressed in Big O notation). Most published algorithms will report an upper bound on the time and space characteristics of the algorithm. You can use that as a comparative indicator, although you need to know a little bit about computational complexity in order to make sure you're not fooling yourself. You could also possibly derive this information from manual inspection of program code, but it's probably not necessary, because this information is almost always published along with the algorithm.
Finally, understanding the algorithm is always a good idea. It makes it easier to know what you need to do as a user of that algorithm to ensure you're getting the best possible results (and indeed to know whether the results you are getting are sensible or not), and it will allow you to apply quality measures such as those I suggested in the first paragraph of this answer.