This presentation discusses the following - Possible strategies to follow when working on a new machine learning problem. The common problems with classifiers (how to detect them and eliminate them). Popular approaches on how to use binary classifiers to problems with multi class classification.

1. Binary class and Multi
Class Strategies for
Machine learning
By Vaibhav Arora

2. Two popular Approaches to algorithm design

3. contd..
Approach 1 the benefit is that we have much better and scalable algorithms we may come up with
new, elegant, learning algorithms and contribute to basic research in machine learning.With
sufficient time at hand this approach should be followed.
Approach 2 Will often get your algorithm working more quickly[3].it has faster time to
market.This approach really gives us a timely feedback and helps us in utilizing our resources
better.

4. What can go wrong?
1) High bias(underfit)
2) high variance(overfit)

5. How to detect it?
There are a no of ways we can do it but i will mention two:-
firstly, If you have a 2d or 3d dataset then you can visualize
the decision boundary and try to figure out what is possibly
wrong with your implementation.
ie if it overly fits the training set but fails on the
validation set then there is an overfitting problem (High
variance).Or if it fails to fit both the training or validation
set then there may be a high bias problem (underfitting).

6. Learning Curves
Secondly, In case you have more than three dimensions we can’t
visualize them so we use learning curves instead to see what
problem we have.
Def:-
A learning curve is a plot of the training and validation error
as a function of the number of training examples.These are meant
to give us important ideas about the problems associated with
our model.

7. LEARNING CURVES Procedure
1) let Xtr denote the training examples and Ytr denote the training targets and
let the no of training examples be n.
2) Then do
for i=1:n
train the learning algorithm on the training subset ie (Xtr(1:i),Ytr(1:i))
pairs
calculate train error
train_error(i)=Training set error(on Xtr(1:i),Ytr(1:i))
calculate validation error
valid_error(i)=validation set error(on complete validation set)
note that if you are using regularization then set regularization parameter to 0
while calculating the train_error and valid_error.
3) plot them

8. Learning Curves (typical plots)

9. contd..

10. What we want ?

11. How can you correct it?
● To Fix high variance
o Get more training examples.
o Get a smaller set of features.
o Tweak algo parameters
● To Fix high Bias
o Get a larger set of features.
o use polynomial features
o Tweak algo parameters

12. Multi class classification strategies
whenever we have a multi class classification problem there are actually many
strategies that we can follow
1) Use a multi class classifier ie a NNet etc
1) Use an no of binary classifiers to solve the problem at hand
we can combine a no of binary classifiers using these strategies:-
a)one vs all
b)one vs one
c)ECOC
There are advantages and disadvantages of both approaches (you can experiment
with both and try to find out!!) but for the purpose of this presentation we are
only going to discuss the latter.

13. one vs all
In a one vs all strategy we would divide the data sets such that a hypothesis
separates one class label from all of the rest.

14. one vs one
In a one vs one strategy we would divide the data sets into pairs (like shown
below) and then do classification.Note that one hypothesis separates only two
classes irrespective of the other classes.

15. How to select the class of a test
example
Now that we have trained a number of classifiers in one vs one
configuration we may choose a class for a training example as
follows(note that this can be done for both the one vs one or
one vs all, for the examples i have considered there are three
hypothesis for both the cases):
a) Feed the training sample to all the three hypothesis
(classifiers)
b) Select the class(label) for which the hypothesis
function is maximum.

16. Error correcting output codes
In an ECOC strategy we try to build classifiers which target
classification between different class subsets ie divide the
main classification problem into sub-classification tasks

17. How do we do that?
1) first use a no of binary classifiers,we make a matrix as
shown(three classifiers C1,C2,C3 and three labels L1,L2 and
L3) ,here one contains positive class and zero contains
negative class.(it is also possible to exclude certain
classes in which case you can use 1,-1,and 0 ,0 indicating
the removed class)

18. contd..
2) Next when we have a new example we feed it to all the
classifiers and the resultant output can then be compared with
the labels (in terms of a distance measure say hamming[1]) and
the label with the minimum hamming distance is assigned to the
training example(if more than one rows are equidistant then
assign arbitrarily).
3)Eg suppose we have a training example t1 and when we feed it
to the classifiers we get output 001 now its difference with
label 1 is 2, with label 2 is 3 and with label 3 is 1 therefore
label 1 will be assigned to the training example.

19. contd..
4) something that we have to keep in mind :-
if n is the no of classifiers and m is the no of labels then
n>logm (base 2)
5) there are also a no of ways by which we select the coding
matrix(the classifier label matrix) one can be through the use
of generator matrix.other possible strategies include learning
from data(via genetic algorithms) .For more info check
references.

20. Personal experience
ok this is all based on personal experience so you may choose to agree or not
1) When working with svms ECOC one vs one seems to work better so try that first
when using svms.
2) With logistic regression try one vs all approach first.
3) When you have a large no of training examples choose to fit logistic
regression as best as possible instead of a support vector machines with
linear kernel because they may work well but you will have a lot of support
vectors (storage).
So with reasonable compromise on accuracy you can do with a lot less
storage.
4) When programming try to implement all these strategies by implementing the
ECOC coding matrix and then you can choose to select the label either by maximum
hypothesis or on the basis of hamming distance.

21. References
1) https://en.wikipedia.org/wiki/Hamming_distance
1) for detail on learning curves and classification strategies prof Andrew ng’s
machine learning course coursera.
1) Prof Andrew ng course slides at
http://cs229.stanford.edu/materials/ML-advice.pdf
1) for ecoc classification a nice paper ,”Error-Correcting Output Coding for
Text Classification”,by Adam Berger, School of Computer Science Carnegie
Mellon University.can be found at
www.cs.cmu.edu/~aberger/pdf/ecoc.pdf

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