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.
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I have some questions about SVM :
1- Why using SVM? or in other words, what causes it to appear?
2- The state Of art (2017)
3- What improvements have they made?
SVM works very well. In many applications, they are still among the best performing algorithms.
We've seen some progress in particular on linear SVMs, that can be trained much faster than kernel SVMs.
Read more literature. Don't expect an exhaustive answer in this QA format. Show more effort on your behalf.
SVM's are most commonly used for classification problems where labeled data is available (supervised learning) and are useful for modeling with limited data. For problems with unlabeled data (unsupervised learning), then support vector clustering is an algorithm commonly employed. SVM tends to perform better on binary classification problems since the decision boundaries will not overlap. Your 2nd and 3rd questions are very ambiguous (and need lots of work!), but I'll suffice it to say that SVM's have found wide range applicability to medical data science. Here's a link to explore more about this: Applications of Support Vector Machine (SVM) Learning in Cancer Genomics
I am intended to do a yes/no classifier. The problem is that the data does not come from me, so I have to work with what I have been given. I have around 150 samples, each sample contains 3 features, these features are continuous numeric variables. I know the dataset is quite small. I would like to make you two questions:
A) What would be the best machine learning algorithm for this? SVM? a neural network? All that I have read seems to require a big dataset.
B)I could make the dataset a little bit bigger by adding some samples that do not contain all the features, only one or two. I have read that you can use sparse vectors in this case, is this possible with every machine learning algorithm? (I have seen them in SVM)
Thanks a lot for your help!!!
My recommendation is to use a simple and straightforward algorithm, like decision tree or logistic regression, although, the ones you refer to should work equally well.
The dataset size shouldn't be a problem, given that you have far more samples than variables. But having more data always helps.
Naive Bayes is a good choice for a situation when there are few training examples. When compared to logistic regression, it was shown by Ng and Jordan that Naive Bayes converges towards its optimum performance faster with fewer training examples. (See section 4 of this book chapter.) Informally speaking, Naive Bayes models a joint probability distribution that performs better in this situation.
Do not use a decision tree in this situation. Decision trees have a tendency to overfit, a problem that is exacerbated when you have little training data.
I am doing research on machine learning. Now I want to test my algorithms with some famous datasets. Since I am a newbie in this area, I can't find other suitable datasets apart from MNIST. I thing MNIST is quite suitable for our research. Does anyone know some similar datasets with MNIST?
P.S I know another handwritten digit dataset that is often used, called USPS dataset. But I need a dataset with more training examples (typically more than 10000 and comparable to the number of training examples in MNIST), so USPS is out of my selection.
The machine learning archive (http://archive.ics.uci.edu/ml/) contains quite a variety of datasets including those, like MINIST, suitable for classification e.g. (http://archive.ics.uci.edu/ml/datasets/Skin+Segmentation).
I can't say which of them would be suitable without knowing what you're trying to demonstrate with your algorithm but anything inside the UCI archive is well known.
You can try Fashion MNIST or Kuzushiji MNIST that have very similar properties to MNIST, but a bit harder to predict. From Fashion MNIST's page:
Seriously, we are talking about replacing MNIST. Here are some good reasons:
MNIST is too easy. Convolutional nets can achieve 99.7% on MNIST. Classic machine learning algorithms can also achieve 97% easily. Check out our side-by-side benchmark for Fashion-MNIST vs. MNIST, and read "Most pairs of MNIST digits can be distinguished pretty well by just one pixel."
MNIST is overused. In this April 2017 Twitter thread, Google Brain research scientist and deep learning expert Ian Goodfellow calls for people to move away from MNIST.
MNIST can not represent modern CV tasks, as noted in this April 2017 Twitter thread, deep learning expert/Keras author François Chollet.
I am working on binary classification of data and I want to know the advantages and disadvantages of using Support vector machine over decision trees and Adaptive Boosting algorithms.
Something you might want to do is use weka, which is a nice package that you can use to plug in your data and then try out a bunch of different machine learning classifiers to see how each works on your particular set. It's a well-tread path for people who do machine learning.
Knowing nothing about your particular data, or the classification problem you are trying to solve, I can't really go beyond just telling you random things I know about each method. That said, here's a brain dump and links to some useful machine learning slides.
Adaptive Boosting uses a committee of weak base classifiers to vote on the class assignment of a sample point. The base classifiers can be decision stumps, decision trees, SVMs, etc.. It takes an iterative approach. On each iteration - if the committee is in agreement and correct about the class assignment for a particular sample, then it becomes down weighted (less important to get right on the next iteration), and if the committee is not in agreement, then it becomes up weighted (more important to classify right on the next iteration). Adaboost is known for having good generalization (not overfitting).
SVMs are a useful first-try. Additionally, you can use different kernels with SVMs and get not just linear decision boundaries but more funkily-shaped ones. And if you put L1-regularization on it (slack variables) then you can not only prevent overfitting, but also, you can classify data that isn't separable.
Decision trees are useful because of their interpretability by just about anyone. They are easy to use. Using trees also means that you can also get some idea of how important a particular feature was for making that tree. Something you might want to check out is additive trees (like MART).
I have two dependent continuous variables and i want to use their combined values to predict the value of a third binary variable. How do i go about discretizing/categorizing the values? I am not looking for clustering algorithms, i'm specifically interested in obtaining 'meaningful' discrete categories i can subsequently use in in a Bayesian classifier.
Pointers to papers, books, online courses, all very much appreciated!
That is the essence of machine learning and problem one of the most studied problem.
Least-square regression, logistic regression, SVM, random forest are widely used for this type of problem, which is called binary classification.
If your goal is to pragmatically classify your data, several libraries are available, like Scikits-learn in python and weka in java. They have a great documentation.
But if you want to understand what's the intrinsics of machine learning, just search (here or on google) for machine learning resources.
If you wanted to be a real nerd, generate a bunch of different possible discretizations and then train a classifier on it, and then characterize the discretizations by features and then run a classifier on that, and see what sort of discretizations are best!?
In general discretizing stuff is more of an art and having a good understanding of what the input variable ranges mean.