Why behaviour of different classifier differ for different data?
Based on what parameters we can decide the good classifier for particular dataset?
For some dataset naive bayes gives better accuracy than SVM classifier
and for other dataset SVM performs better than naive bayes. Why is it
so? What is the reason?
Those are completely different classifiers. If you would have one classifier which is allways better than the other one. Why would you need the "bad one" then?
First google hit about when SVM's are not the best choice:
https://www.quora.com/For-what-kind-of-classification-problems-is-SVM-a-bad-approach
There is no general answer for this question. To understand which classifier to be used when you will need to understand the algorithm behind the classification procedure.
For instance logistic regression assumes a normal distribution of y and is generally useful when a particular parameter is not a uniquely deciding factor however combined weightage of the factors make a difference, for instance in text classification.
Decision tree on the other hand splits on the basis of parameter which gives most information. So if you have a set of parameters which is highly correlated with the label, then it makes more sense to use decision tree based classifiers.
SVM, work based on identifying adequate hyperplanes. These are generally useful when it is not possible to classify data in one plane but projecting them into higher plane classifies them easily. This is a nice tutorial on SVM https://blog.statsbot.co/support-vector-machines-tutorial-c1618e635e93
In short the only way to learn which classifier will be better in which situation is to understand how they work, and then figure out if they are best for your situation.
Another, crude way will be try every classifier and pick the best one, but i don't think you are interested in that.
Related
What are the advantages and disadvantages of LDA vs Naive Bayes in
terms of machine learning classification?
I know some of the differences like Naive Bayes assumes variables to be independent, while LDA assumes Gaussian class-conditional density models, but I don't understand when to use LDA and when to use NB depending on the situation?
Both methods are pretty simple, so it's hard to say which one is going to work much better. It's often faster just to try both and calculate the test accuracy. But here's the list of characteristics that usually indicate if certain method is less likely to give good results. It all boils down to the data.
Naive Bayes
The first disadvantage of the Naive Bayes classifier is the feature independence assumption. In practice, the data is multi-dimensional and different features do correlate. Due to this, the result can be potentially pretty bad, though not always significantly. If you know for sure, that features are dependent (e.g. pixels of an image), don't expect Naive Bayes to show off.
Another problem is data scarcity. For any possible value of a feature, a likelihood is estimated by a frequentist approach. This can result in probabilities being close to 0 or 1, which in turn leads to numerical instabilities and worse results.
A third problem arises for continuous features. The Naive Bayes classifier works only with categorical variables, so one has to transform continuous features to discrete, by which throwing away a lot of information. If there's a continuous variable in the data, it's a strong sign against Naive Bayes.
Linear Discriminant Analysis
The LDA does not work well if the classes are not balanced, i.e. the number of objects in various classes are highly different. The solution is to get more data, which can be pretty easy or almost impossible, depending on a task.
Another disadvantage of LDA is that it's not applicable for non-linear problems, e.g. separation of donut-shape point clouds, but in high dimensional spaces it's hard to spot it right away. Usually you understand this after you see LDA not working, but if the data is known to be very non-linear, this is a strong sign against LDA.
In addition, LDA can be sensitive to overfitting and need careful validation / testing.
Which are the fundamental criterias for using supervised or unsupervised learning?
When is one better than the other?
Is there specific cases when you can only use one of them?
Thanks
If you a have labeled dataset you can use both. If you have no labels you only can use unsupervised learning.
It´s not a question of "better". It´s a question of what you want to achieve. E.g. clustering data is usually unsupervised – you want the algorithm to tell you how your data is structured. Categorizing is supervised since you need to teach your algorithm what is what in order to make predictions on unseen data.
See 1.
On a side note: These are very broad questions. I suggest you familiarize yourself with some ML foundations.
Good podcast for example here: http://ocdevel.com/podcasts/machine-learning
Very good book / notebooks by Jake VanderPlas: http://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/Index.ipynb
Depends on your needs. If you have a set of existing data including the target values that you wish to predict (labels) then you probably need supervised learning (e.g. is something true or false; or does this data represent a fish or cat or a dog? Simply put - you already have examples of right answers and you are just telling the algorithm what to predict). You also need to distinguish whether you need a classification or regression. Classification is when you need to categorize the predicted values into given classes (e.g. is it likely that this person develops a diabetes - yes or no? In other words - discrete values) and regression is when you need to predict continuous values (1,2, 4.56, 12.99, 23 etc.). There are many supervised learning algorithms to choose from (k-nearest neighbors, naive bayes, SVN, ridge..)
On contrary - use the unsupervised learning if you don't have the labels (or target values). You're simply trying to identify the clusters of data as they come. E.g. k-Means, DBScan, spectral clustering..)
So it depends and there's no exact answer but generally speaking you need to:
Collect and see you data. You need to know your data and only then decide which way you choose or what algorithm will best suite your needs.
Train your algorithm. Be sure to have a clean and good data and bear in mind that in case of unsupervised learning you can skip this step as you don't have the target values. You test your algorithm right away
Test your algorithm. Run and see how well your algorithm behaves. In case of supervised learning you can use some training data to evaluate how well is your algorithm doing.
There are many books online about machine learning and many online lectures on the topic as well.
Depends on the data set that you have.
If you have target feature in your hand then you should go for supervised learning. If you don't have then it is a unsupervised based problem.
Supervised is like teaching the model with examples. Unsupervised learning is mainly used to group similar data, it plays a major role in feature engineering.
Thank you..
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 new to Machine learning and I have this basic question. As I am weak in Math part of the algorithm I find it difficult to understand this.
When you are given a task to design a classifier(keep it simple -- a 2 class classifier) using unsupervised learning(no training samples), how to decide what type of classifier(linear or non-linear) to use? If we do not know this, then the importance on feature selection(which means indirectly knowing what the data set is) becomes very critical.
Am I thinking in the right direction or is there something big that I dont know. Insight into this topic is greatly appreciated.
classification is by definition a "supervised learning" problem. such models require examples of points within given classes to understand how to separate the classes from one another. if you are simply looking for relationships between unlabeled data points, you're solving an unsupervised problem. look into clustering algorithms. k-means is where a lot of people start.
hope this helps!
This is a huge problem. Yes, the term "clustering" is the best entry point for googling about that, but I understand that you want to train a classifier, where "training" means optimizing an objective function with parameters. The first choice is definitely not discriminative classifiers (such as linear ones), because with them, the standard maximum likelihood (ML) objective does not work without labels. If you absolutely want to use linear classifiers, then you have to tweak the ML objective, or better use another objective (approximating the classifier risk). But an easier choice is to rather look at generative models, such as HMMs, Naive Bayes, Latent Dirichlet Allocation, ... for which the ML objective works without labels.
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).