While going through one kernel on Kaggle regarding Regression in that it was mentioned that the data should look like a normal distribution. But I am not getting why?
I know this question might be very basic But please help me to understand this concept.
Thanks in Advance!!
Regression models make a number of assumptions, one of which is normality. When this assumption is violated then your p-values and confidence intervals around your coefficient estimate could be wrong, leading to incorrect conclusions about the statistical significance of your predictors
However, a common misconception is that the data (i.e. the variables/predictors) needs to be normally distributed, but this is not true. These models don't make any assumptions about the distribution of predictors.
For example, imagine a case where you have a binary predictor in regression (Male/Female; Slow/Fast etc.) - it would be impossible for this variable to be normally distributed and yet it is still a valid predictor to use in a regression model. The normality assumption actually refers to the distribution of the residuals, not the predictors themselves
Wikipedia states three methods of feature scaling. Which should be used when? (what are the considerations?)
To be specific, I need it for Sentiment Analysis on phrases, implemented with SVM.
(NOTE: I've seen this post. It explains the different methods quite well, but doesn't say anything about when each should be used).
Thank you :)
Actually this is quite hard to give any reasonable rules for selecting scaling over standarization. Standarization of your data has a good theoretical justification and is less influenced by outliers than scaling. As the result the most commonly used method of preprocessing is standarization.
In particular, if you ask about standarization than you use some kind of bag of words representation of your data. In such a case tf-idf is the most obvious selection of the data representation, which in fact is hardly influenced by any scaling/standarization as it is quite well standarized itself (due to the internal normalization and log-scaling).
I am implementing K nearest neighbour algorithm for a very sparse data. I want to calculate the distance between a test instance and each sample in the training set, but I am confused.
Because most of the features in training samples don't exist in test instance or vice versa (missing features).
How can I compute the distance in this situation?
To make sure I'm understanding the problem correctly: each sample forms a very sparsely filled vector. The missing data is different between samples, so it's hard to use any Euclidean or other distance metric to gauge similarity of samples.
If that is the scenario, I have seen this problem show up before in machine learning - in the Netflix prize contest, but not specifically applied to KNN. The scenario there was quite similar: each user profile had ratings for some movies, but almost no user had seen all 17,000 movies. The average user profile was quite sparse.
Different folks had different ways of solving the problem, but the way I remember was that they plugged in dummy values for the missing values, usually the mean of the particular value across all samples with data. Then they used Euclidean distance, etc. as normal. You can probably still find discussions surrounding this missing value problem on that forums. This was a particularly common problem for those trying to implement singular value decomposition, which became quite popular and so was discussed quite a bit if I remember right.
You may wish to start here:
http://www.netflixprize.com//community/viewtopic.php?id=1283
You're going to have to dig for a bit. Simon Funk had a little different approach to this, but it was more specific to SVDs. You can find it here: http://www.netflixprize.com//community/viewtopic.php?id=1283
He calls them blank spaces if you want to skip to the relevant sections.
Good luck!
If you work in very high dimension space. It is better to do space reduction using SVD, LDA, pLSV or similar on all available data and then train algorithm on trained data transformed that way. Some of those algorithms are scalable therefor you can find implementation in Mahout project. Especially I prefer using more general features then such transformations, because it is easier debug and feature selection. For such purpose combine some features, use stemmers, think more general.
Consider an optimization problem of some dimension n, Given some linear set of equations(inequalities) or constraints on the inputs which form a convex region, finding the maximum\minimum value of some expression which is some linear combination of inputs(or dimensions).
For larger dimension, these optimization problems take much time to give the exact answer.
So, can we use machine learning techniques, to get some approximate solution in lesser time.
if we can use machine learning techniques in this context, How the Training set should be??
Do you mean "How big should the training set be?" If so, then that is very much a "how long is a piece of string" question. It needs to be large enough for the algorithm being used, and to represent the data that is being modeled.
This doesn't strike me as being especially focused on machine learning, as is typically meant by the term anyway. It's just a straightforward constrained optimization problem. You say that it takes too long to find solutions now, but you don't mention how you're trying to solve the problem.
The simplex algorithm is designed for this sort of problem, but it's exponential in the worst case. Is that what you're trying that's taking too long? If so, there are tons of metaheuristics that might perform well. Tabu search, simulated annealing, evolutionary algorithms, variable depth search, even simple multistart hill climbers. I would probably try something along those lines before I tried anything exotic.
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I am wanting some expert guidance here on what the best approach is for me to solve a problem. I have investigated some machine learning, neural networks, and stuff like that. I've investigated weka, some sort of baesian solution.. R.. several different things. I'm not sure how to really proceed, though. Here's my problem.
I have, or will have, a large collection of events.. eventually around 100,000 or so. Each event consists of several (30-50) independent variables, and 1 dependent variable that I care about. Some independent variables are more important than others in determining the dependent variable's value. And, these events are time relevant. Things that occur today are more important than events that occurred 10 years ago.
I'd like to be able to feed some sort of learning engine an event, and have it predict the dependent variable. Then, knowing the real answer for the dependent variable for this event (and all the events that have come along before), I'd like for that to train subsequent guesses.
Once I have an idea of what programming direction to go, I can do the research and figure out how to turn my idea into code. But my background is in parallel programming and not stuff like this, so I'd love to have some suggestions and guidance on this.
Thanks!
Edit: Here's a bit more detail about the problem that I'm trying to solve: It's a pricing problem. Let's say that I'm wanting to predict prices for a random comic book. Price is the only thing I care about. But there are lots of independent variables one could come up with. Is it a Superman comic, or a Hello Kitty comic. How old is it? What's the condition? etc etc. After training for a while, I want to be able to give it information about a comic book I might be considering, and have it give me a reasonable expected value for the comic book. OK. So comic books might be a bogus example. But you get the general idea. So far, from the answers, I'm doing some research on Support vector machines and Naive Bayes. Thanks for all of your help so far.
Sounds like you're a candidate for Support Vector Machines.
Go get libsvm. Read "A practical guide to SVM classification", which they distribute, and is short.
Basically, you're going to take your events, and format them like:
dv1 1:iv1_1 2:iv1_2 3:iv1_3 4:iv1_4 ...
dv2 1:iv2_1 2:iv2_2 3:iv2_3 4:iv2_4 ...
run it through their svm-scale utility, and then use their grid.py script to search for appropriate kernel parameters. The learning algorithm should be able to figure out differing importance of variables, though you might be able to weight things as well. If you think time will be useful, just add time as another independent variable (feature) for the training algorithm to use.
If libsvm can't quite get the accuracy you'd like, consider stepping up to SVMlight. Only ever so slightly harder to deal with, and a lot more options.
Bishop's Pattern Recognition and Machine Learning is probably the first textbook to look to for details on what libsvm and SVMlight are actually doing with your data.
If you have some classified data - a bunch of sample problems paired with their correct answers -, start by training some simple algorithms like K-Nearest-Neighbor and Perceptron and seeing if anything meaningful comes out of it. Don't bother trying to solve it optimally until you know if you can solve it simply or at all.
If you don't have any classified data, or not very much of it, start researching unsupervised learning algorithms.
It sounds like any kind of classifier should work for this problem: find the best class (your dependent variable) for an instance (your events). A simple starting point might be Naive Bayes classification.
This is definitely a machine learning problem. Weka is an excellent choice if you know Java and want a nice GPL lib where all you have to do is select the classifier and write some glue. R is probably not going to cut it for that many instances (events, as you termed it) because it's pretty slow. Furthermore, in R you still need to find or write machine learning libs, though this should be easy given that it's a statistical language.
If you believe that your features (independent variables) are conditionally independent (meaning, independent given the dependent variable), naive Bayes is the perfect classifier, as it is fast, interpretable, accurate and easy to implement. However, with 100,000 instances and only 30-50 features you can likely implement a fairly complex classification scheme that captures a lot of the dependency structure in your data. Your best bet would probably be a support vector machine (SMO in Weka) or a random forest (Yes, it's a silly name, but it helped random forest catch on.) If you want the advantage of easy interpretability of your classifier even at the expense of some accuracy, maybe a straight up J48 decision tree would work. I'd recommend against neural nets, as they're really slow and don't usually work any better in practice than SVMs and random forest.
The book Programming Collective Intelligence has a worked example with source code of a price predictor for laptops which would probably be a good starting point for you.
SVM's are often the best classifier available. It all depends on your problem and your data. For some problems other machine learning algorithms might be better. I have seen problems that neural networks (specifically recurrent neural networks) were better at solving. There is no right answer to this question since it is highly situationally dependent but I agree with dsimcha and Jay that SVM's are the right place to start.
I believe your problem is a regression problem, not a classification problem. The main difference: In classification we are trying to learn the value of a discrete variable, while in regression we are trying to learn the value of a continuous one. The techniques involved may be similar, but the details are different. Linear Regression is what most people try first. There are lots of other regression techniques, if linear regression doesn't do the trick.
You mentioned that you have 30-50 independent variables, and some are more important that the rest. So, assuming that you have historical data (or what we called a training set), you can use PCA (Principal Componenta Analysis) or other dimensionality reduction methods to reduce the number of independent variables. This step is of course optional. Depending on situations, you may get better results by keeping every variables, but add a weight to each one of them based on relevant they are. Here, PCA can help you to compute how "relevant" the variable is.
You also mentioned that events that are occured more recently should be more important. If that's the case, you can weight the recent event higher and the older event lower. Note that the importance of the event doesn't have to grow linearly accoding to time. It may makes more sense if it grow exponentially, so you can play with the numbers here. Or, if you are not lacking of training data, perhaps you can considered dropping off data that are too old.
Like Yuval F said, this does look more like a regression problem rather than a classification problem. Therefore, you can try SVR (Support Vector Regression), which is regression version of SVM (Support Vector Machine).
some other stuff you can try are:
Play around with how you scale the value range of your independent variables. Say, usually [-1...1] or [0...1]. But you can try other ranges to see if they help. Sometimes they do. Most of the time they don't.
If you suspect that there are "hidden" feature vector with a lower dimension, say N << 30 and it's non-linear in nature, you will need non-linear dimensionality reduction. You can read up on kernel PCA or more recently, manifold sculpting.
What you described is a classic classification problem. And in my opinion, why code fresh algorithms at all when you have a tool like Weka around. If I were you, I would run through a list of supervised learning algorithms (I don't completely understand whey people are suggesting unsupervised learning first when this is so clearly a classification problem) using 10-fold (or k-fold) cross validation, which is the default in Weka if I remember, and see what results you get! I would try:
-Neural Nets
-SVMs
-Decision Trees (this one worked really well for me when I was doing a similar problem)
-Boosting with Decision trees/stumps
-Anything else!
Weka makes things so easy and you really can get some useful information. I just took a machine learning class and I did exactly what you're trying to do with the algorithms above, so I know where you're at. For me the boosting with decision stumps worked amazingly well. (BTW, boosting is actually a meta-algorithm and can be applied to most supervised learning algs to usually enhance their results.)
A nice thing aobut using Decision Trees (if you use the ID3 or similar variety) is that it chooses the attributes to split on in order of how well they differientiate the data - in other words, which attributes determine the classification the quickest basically. So you can check out the tree after running the algorithm and see what attribute of a comic book most strongly determines the price - it should be the root of the tree.
Edit: I think Yuval is right, I wasn't paying attention to the problem of discretizing your price value for the classification. However, I don't know if regression is available in Weka, and you can still pretty easily apply classification techniques to this problem. You need to make classes of price values, as in, a number of ranges of prices for the comics, so that you can have a discrete number (like 1 through 10) that represents the price of the comic. Then you can easily run classification it.