I'm having problems in understanding how K-NN classification works in MATLAB.´
Here's the problem, I have a large dataset (65 features for over 1500 subjects) and its respective classes' label (0 or 1).
According to what's been explained to me, I have to divide the data into training, test and validation subsets to perform supervised training on the data, and classify it via K-NN.
First of all, what's the best ratio to divide the 3 subgroups (1/3 of the size of the dataset each?).
I've looked into ClassificationKNN/fitcknn functions, as well as the crossval function (idealy to divide data), but I'm really not sure how to use them.
To sum up, I wanted to
- divide data into 3 groups
- "train" the KNN (I know it's not a method that requires training, but the equivalent to training) with the training subset
- classify the test subset and get it's classification error/performance
- what's the point of having a validation test?
I hope you can help me, thank you in advance
EDIT: I think I was able to do it, but, if that's not asking too much, could you see if I missed something? This is my code, for a random case:
nfeats=60;ninds=1000;
trainRatio=0.8;valRatio=.1;testRatio=.1;
kmax=100; %for instance...
data=randi(100,nfeats,ninds);
class=randi(2,1,ninds);
[trainInd,valInd,testInd] = dividerand(1000,trainRatio,valRatio,testRatio);
train=data(:,trainInd);
test=data(:,testInd);
val=data(:,valInd);
train_class=class(:,trainInd);
test_class=class(:,testInd);
val_class=class(:,valInd);
precisionmax=0;
koptimal=0;
for know=1:kmax
%is it the same thing use knnclassify or fitcknn+predict??
predicted_class = knnclassify(val', train', train_class',know);
mdl = fitcknn(train',train_class','NumNeighbors',know) ;
label = predict(mdl,val');
consistency=sum(label==val_class')/length(val_class);
if consistency>precisionmax
precisionmax=consistency;
koptimal=know;
end
end
mdl_final = fitcknn(train',train_class','NumNeighbors',know) ;
label_final = predict(mdl,test');
consistency_final=sum(label==test_class')/length(test_class);
Thank you very much for all your help
For your 1st question "what's the best ratio to divide the 3 subgroups" there are only rules of thumb:
The amount of training data is most important. The more the better.
Thus, make it as big as possible and definitely bigger than the test or validation data.
Test and validation data have a similar function, so it is convenient to assign them the same amount
of data. But it is important to have enough data to be able to recognize over-adaptation. So, they
should be picked from the data basis fully randomly.
Consequently, a 50/25/25 or 60/20/20 partitioning is quite common. But if your total amount of data is small in relation to the total number of weights of your chosen topology (e.g. 10 weights in your net and only 200 cases in the data), then 70/15/15 or even 80/10/10 might be better choices.
Concerning your 2nd question "what's the point of having a validation test?":
Typically, you train the chosen model on your training data and then estimate the "success" by applying the trained model to unseen data - the validation set.
If you now would completely stop your efforts to improve accuracy, you indeed don't need three partitions of your data. But typically, you feel that you can improve the success of your model by e.g. changing the number of weights or hidden layers or ... and now a big loops starts to run with many iterations:
1) change weights and topology, 2) train, 3) validate, not satisfied, goto 1)
The long-term effect of this loop is, that you increasingly adapt your model to the validation data, so the results get better not because you so intelligently improve your topology but because you unconsciously learn the properties of the validation set and how to cope with them.
Now, the final and only valid accuracy of your neural net is estimated on really unseen data: the test set. This is done only once and is also useful to reveal over-adaption. You are not allowed to start a second even bigger loop now to prohibit any adaption to the test set!
Related
train_image_paths = [str(path) for path in list(train_path.glob('*/*.jpeg'))]
random.shuffle(train_image_paths)
Above is a sample code you can see.
I have the same question in this case too:
train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train)).batch(64).shuffle(10000)
I don't understand why I need the shuffle in these cases.
In the obvious case, shuffling is helpful if your training data is sorted by class labels. By shuffling, you allow your model to "see" a wide range of data points each belonging to different classes in the context of classification. If the model goes through a sorted training data, your model runs the risk of overfitting to certain classes. In short, shuffling helps reduce variance and ensures that the train, test, and validation sets are representative of the true distribution.
I made a neural network whice i want to classify the input data (400 caracteristics per input data) as one of the five arabic dialects. I divede the trainig data in "train data", "validation data" and than "test date", with net.divideFcn = 'dividerand'; . I use trainbr as training function, whice results in a long training, that's because i have 9000 elements in training data.
For the network arhitecture i used two-layers, first with 10 perceptrons, second with 5, 5 because i use one vs all strategy.
The network training ends usually with minimum gradient reached, rather than minimux error.
How can i make the network predict better? Could it be o problem with generalization (the network learn very well the training data, but test on new data tends to fail?
Should i add more perceptrons to the first layer? I'm asking that because i take about a hour to train the network when i have 10 perceptrons on first layer, so the time will increase.
This is the code for my network:
[Test] = load('testData.mat');
[Ex] = load('trainData.mat');
Ex.trainVectors = Ex.trainVectors';
Ex.trainLabels = Ex.trainLabels';
net = newff(minmax(Ex.trainVectors),[10 5] ,{'logsig','logsig'},'trainlm','learngdm','sse');
net.performFcn = 'mse';
net.trainParam.lr = 0.01;
net.trainParam.mc = 0.95;
net.trainParam.epochs = 1000;
net.trainParam.goal = 0;
net.trainParam.max_fail = 50;
net.trainFcn = 'trainbr';
net.divideFcn = 'dividerand';
net.divideParam.trainRatio = 0.7;
net.divideParam.valRatio = 0.15;
net.divideParam.testRatio = 0.15;
net = init(net);
net = train(net,Ex.trainVectors,Ex.trainLabels);
Thanks !
Working with neural networks is some type of creative work. So noone can't give you the only true answer. But I can give some advices based on my own experience.
First of all - check the network error when training ends (on training and validation data sets. Before you start to use test data set). You told it is minimum but what is its actual value? If it 50% too, so we have bad data or wrong net architecture.
If error for train data set is OK. Next step - lets check how much the coefficients of your net are changing at the validation step. And what's up about the error here. If they changed dramatically that's the sigh our architecture is wrong: Network does not have the ability to generalize and will retrain at every new data sets.
What else can we do before changing architecture? We can change the number of epochs. Sometimes we can get good results but it is some type of random - we must be sure the changing of coefficient is small at the ending steps of training. But as I remember nntool check it automatically, so maybe we can skip this step.
One more thing I want to recommend to you - change train data set. Maybe you know rand is give you always the same number at start of matlab, so if you create your data sets only once you can work with the same sets always. This problem is also about non-homogeneous data. It can be that some part of your data is more important than other. So if some different random sets will give about the same error data is ok and we can go further. If not - we need to work with data and split it more carefully. Sometimes I avoid using dividerand and divide data manually.
Sometimes I tried to change the type of activation function. But here you use perceptron... So the idea - try to use sigma- or linear- neurons instead of perceptrons. This rarely leads to significant improvements but can help.
If all this steps can't give you enough you have to change net architecture. And the number of neurons in the first layer is the first you have to do. Usually when I work on the neural network I spend a lot of time trying not only different number of neurons but the different types of nets too.
For example, I found interesting article about your topic: link at Alberto Simões article. And that's what they say:
Regarding the number of units in the hidden layers, there are some
rules of thumb: use the same number of units in all hidden layers, and
use at least the same number of units as the maximum between the
number of classes and the number of features. But there can be up to
three times that value. Given the high number of features we opted to
keep that same number of units in the hidden layer.
Some advices from comments:
Data split method (for train and test data sets) depends on your data. For example, I worked on industry data and found that at the last part of the data set technological parameters (pressure for some equipment) was changed. So I have to get data for both operation modes to train data set. But for your case I don't thing there are the same problem... I recommend you to try several random sets (just check they are really different!).
For measuring net error I usually calculate full vector of errors - I train net and then check it's work for all values to get the whole errors vector. It's useful to get some useful vies like histograms and etc and I can see where my net is go wrong. It is not necessary and even harmful to get sse (or mse) close to zero - usually that's mean you already overtrain the net. For the first approximation I usually try to get 80-95% of correct values on training data set and then try the net on the test data set.
I have been working on "Script identification from bilingual documents".
I want to classify the pages/blocks as either Eng(class 1), Hindi (class 2) or Mixed using libsvm in matlab. but the problem is that the training data i have consists of samples corresponding to Hindi and english pages/blocks only but no mixed pages.
The test data i want to give may consists of Mixed pages/blocks also, in that case i want it to be classified as "Mixed". I am planning to do it using confidence score or probability values. like if the prob value of class 1 is greater than a threshold (say 0.8) and prob value of class 2 is less than a threshold say(0.05) then it will be classified as class 1, and class 2 vice-versa. but if aforementioned two conditions dont satisfy then i want to classify it as "Mixed".
The third return value from the "libsvmpredict" is prob_values and i was planning to go ahead with this prob_values to decide whether the testdata is Hindi, English or Mixed. but at few places i learnt that "libsvmpredict" does not produce the actual prob_values.
Is there any way which can help me to classify the test data into 3 classes( Hindi, English, Mixed) using training data consisting of only 2 classes in SVM.
This is not the modus operandi for SVMs.
In no way SVMs can predict a given class without knowing it, without knowing how to separate such class from all other classes.
The function svmpredict() in LibSVM actually shows the probability estimates and the greater this value is, the more confident you can be regarding your prediction. But you cannot rely on such values if you have just two classes in order to predict a third class: indeed svmpredict() will return as many decision values as there are classes.
You can go on with your thresholding system (which, again, is not SVM-based) but it most likely fail or give bad performances. Think about that: you have to set up two thresholds and use them in a logic AND manner. The chance of correctly classified non-Mixed documents will indeed drastically decrease.
My suggestion is: instead of wasting time setting up thresholds, with a high chance of bad performances, join some of these texts together or create some new files with some Hindi and some English lines in order to add to your training data some proper Mixed documents and perform a standard 3-classes SVM system.
In order to create such files you can as well use Matlab, which has a pretty decent file I/O functions such as fread(), fwrite(), fprintf(), fscanf(), importdata() and so on...
Just starting to play around with Neural Networks for fun after playing with some basic linear regression. I am an English teacher so don't have a math background and trying to read a book on this stuff is way over my head. I thought this would be a better avenue to get some basic questions answered (even though I suspect there is no easy answer). Just looking for some general guidance put in layman's terms. I am using a trial version of an Excel Add-In called NEURO XL. I apologize if these questions are too "elementary."
My first project is related to predicting a student's Verbal score on the SAT based on a number of test scores, GPA, practice exam scores, etc. as well as some qualitative data (gender: M=1, F=0; took SAT prep class: Y=1, N=0; plays varsity sports: Y=1, N=0).
In total, I have 21 variables that I would like to feed into the network, with the output being the actual score (200-800).
I have 9000 records of data spanning many years/students. Here are my questions:
How many records of the 9000 should I use to train the network?
1a. Should I completely randomize the selection of this training data or be more involved and make sure I include a variety of output scores and a wide range of each of the input variables?
If I split the data into an even number, say 9x1000 (or however many) and created a network for each one, then tested the results of each of these 9 on the other 8 sets to see which had the lowest MSE across the samples, would this be a valid way to "choose" the best network if I wanted to predict the scores for my incoming students (not included in this data at all)?
Since the scores on the tests that I am using as inputs vary in scale (some are on 1-100, and others 1-20 for example), should I normalize all of the inputs to their respective z-scores? When is this recommended vs not recommended?
I am predicting the actual score, but in reality, I'm NOT that concerned about the exact score but more of a range. Would my network be more accurate if I grouped the output scores into buckets and then tried to predict this number instead of the actual score?
E.g.
750-800 = 10
700-740 = 9
etc.
Is there any benefit to doing this or should I just go ahead and try to predict the exact score?
What if ALL I cared about was whether or not the score was above or below 600. Would I then just make the output 0(below 600) or 1(above 600)?
5a. I read somewhere that it's not good to use 0 and 1, but instead 0.1 and 0.9 - why is that?
5b. What about -1(below 600), 0(exactly 600), 1(above 600), would this work?
5c. Would the network always output -1, 0, 1 - or would it output fractions that I would then have to roundup or rounddown to finalize the prediction?
Once I have found the "best" network from Question #3, would I then play around with the different parameters (number of epochs, number of neurons in hidden layer, momentum, learning rate, etc.) to optimize this further?
6a. What about the Activation Function? Will Log-sigmoid do the trick or should I try the other options my software has as well (threshold, hyperbolic tangent, zero-based log-sigmoid).
6b. What is the difference between log-sigmoid and zero-based log-sigmoid?
Thanks!
First a little bit of meta content about the question itself (and not about the answers to your questions).
I have to laugh a little that you say 'I apologize if these questions are too "elementary."' and then proceed to ask the single most thorough and well thought out question I've seen as someone's first post on SO.
I wouldn't be too worried that you'll have people looking down their noses at you for asking this stuff.
This is a pretty big question in terms of the depth and range of knowledge required, especially the statistical knowledge needed and familiarity with Neural Networks.
You may want to try breaking this up into several questions distributed across the different StackExchange sites.
Off the top of my head, some of it definitely belongs on the statistics StackExchange, Cross Validated: https://stats.stackexchange.com/
You might also want to try out https://datascience.stackexchange.com/ , a beta site specifically targeting machine learning and related areas.
That said, there is some of this that I think I can help to answer.
Anything I haven't answered is something I don't feel qualified to help you with.
Question 1
How many records of the 9000 should I use to train the network? 1a. Should I completely randomize the selection of this training data or be more involved and make sure I include a variety of output scores and a wide range of each of the input variables?
Randomizing the selection of training data is probably not a good idea.
Keep in mind that truly random data includes clusters.
A random selection of students could happen to consist solely of those who scored above a 30 on the ACT exams, which could potentially result in a bias in your result.
Likewise, if you only select students whose SAT scores were below 700, the classifier you build won't have any capacity to distinguish between a student expected to score 720 and a student expected to score 780 -- they'll look the same to the classifier because it was trained without the relevant information.
You want to ensure a representative sample of your different inputs and your different outputs.
Because you're dealing with input variables that may be correlated, you shouldn't try to do anything too complex in selecting this data, or you could mistakenly introduce another bias in your inputs.
Namely, you don't want to select a training data set that consists largely of outliers.
I would recommend trying to ensure that your inputs cover all possible values for all of the variables you are observing, and all possible results for the output (the SAT scores), without constraining how these requirements are satisfied.
I'm sure there are algorithms out there designed to do exactly this, but I don't know them myself -- possibly a good question in and of itself for Cross Validated.
Question 3
Since the scores on the tests that I am using as inputs vary in scale (some are on 1-100, and others 1-20 for example), should I normalize all of the inputs to their respective z-scores? When is this recommended vs not recommended?
My understanding is that this is not recommended as the input to a Nerual Network, but I may be wrong.
The convergence of the network should handle this for you.
Every node in the network will assign a weight to its inputs, multiply them by their weights, and sum those products as a core part of its computation.
That means that every node in the network is searching for some coefficients for each of their inputs.
To do this, all inputs will be converted to numeric values -- so conditions like gender will be translated into "0=MALE,1=FEMALE" or something similar.
For example, a node's metric might look like this at a given point in time:
2*ACT_SCORE + 0*GENDER + (-5)*VARISTY_SPORTS ...
The coefficients for each values are exactly what the network is searching for as it converges.
If you change the scale of a value, like ACT_SCORE, you just change the scale of the coefficient that will be found by the reciporical of that scaling factor.
The result should still be the same.
There are other concerns in terms of accuracy (computers have limited capacity to represent small fractions) and speed that may enter this, but not being familiar with NEURO XL, I can't say whether or not they apply for this technology.
Question 4
I am predicting the actual score, but in reality, I'm NOT that concerned about the exact score but more of a range. Would my network be more accurate if I grouped the output scores into buckets and then tried to predict this number instead of the actual score?
This will reduce accuracy, although you should converge to a solution much faster with fewer possible outputs (scores).
Neural Networks actually describe very high-dimensional functions in their input variables.
If you reduce the granularity of that function's output space, you essentially state that you don't care about local minima and maxima in that function, especially around the borders between your output scores.
As a result, you are sacrificing information that may be an essential component of the "true" function that you are searching for.
I hope this has been helpful, but you really should break this question down into its many components and ask them separately on different sites -- potentially some of them do belong here on StackOverflow as well.
I'm using mahout 0.7 to do some classification. I have an encoder for a continuous variable
ContinuousValueEncoder durationPlanEncoder = new ContinuousValueEncoder("duration_plan");
The feature associated with this encoder is a number of days and can range from about 6 to 16.
I'm using an OnlineLogisticRegression model and I use the encoder to train it:
durationPlanEncoder.addToVector(null, <duration_plan double val>, trainDataVector);
For simplicity (since i'm trying to understand this whole classification thing while also learning Mahout), i am using 2 variables: 1) a categorical variable with 6 categories -- one of which ("dev") always predicts the =1 category; and 2) this "duration_plan" variable.
What i expect to find is that, when i give the classifier test data that consists of the category "dev" and a "duration_plan" value, the accuracy of the classifier will increase as the "duration_plan" value i give it gets closer to its average value across the training data. This is not what i'm seeing, however. Instead, the accuracy of the classifier improves as the value of "duration_plan" goes to 0.0. However -- there are no training vectors with duration_plan=0.0!! Why would this be the case?
Then i modified my durationPlanEncoder as follows:
durationPlanEncoder.setProbes(2);
and the accuracy improved. It got even better when i made the number of probes 20, then 200. Why? What is setProbes() doing and is this an anomaly or is this actually how i should be doing it?
The final part of my question is to mention that, even after setting setProbes(20), changing the value of "duration_plan" in the test data has no effect on the accuracy of the classifier -- which I don't think is how it should be. If i give a value for duration_plan that doesn't even exist in any of the training data and thus is never correlated with the =1 class, i would expect the classifier to classify the test sample as =0. Right? Which makes me think i must be coding something just plain wrong. Any suggestions are appreciated.
Mahout documentation is woefully sparse.
thanks.