I'm now in the middle of the semester and trying to understand the background of the algorithms and features.
I would like to understand some theory.
If I have a dataset with N samples.
each sample has 5 features for example.
I have done 3 kinds of classifications algorithms for example : SVM, decision tree and kMeans.
In all 3, I got nice results
In a mystery way, a new feature added to the dataset. The value of the features for every sample selected randomly.
I restarted the algorithms on the dataset ( with the new feature)
Are the classification results gonna change from the first results without the new feature? If yes, why are they gonna change and by how much ?
In addition, if I do not have the dataset how can I know how to recognize that new feature?
The results of your classification algorithm are going to either change or stay the same depending on how much information the model gains from the feature. If the feature for instance is random noise then it will have little to no effect on your model, other than slowing it down. If it contains useful information it might be able to increase parameters such as recall and precision. Hope this might help.
Related
Im creating my first predictive model and its results are absolutely awful.
Im in need of some help identifying how i troubleshoot this.
Im doing linear regression & logistic regression classification, to predict if a student will pass a course, 1 for yes, 0 for no.
The dataset is tiny, as we only have complete data for one class, 16 features just under 60 rows, 35 passed and 25 failed.
I'm wondering if my dataset is simply too small.
I dont want to share the dataset just yet, but will clean it up so its completely anonymous.
The ROC is very very jagged and mostly (for log regression), and predicts more false positives than anything else.
Id appreciate some general troubleshooting advice for a beginner that i can try before we hire in a professional.
Thanks for any help provided.
Id suggest some tips:
In Azure ML theres a module called "filter based feature selection", you can use it to score your features and check if there is really predictive power in them or even select just the ones with the highest score.
If you haven't ,splitt in train/cross validation set and evaluate your model in both and use it as a diagnosis to identify underfitting(high bias) or overfitting(high variance), and depending on the diagnosis perform actions like:
For overfitting: get more data, use less features, use a less complex model , add or increase regularization
For underfitting: add more features, use a more complex model, decrease regularization.
And don't forget ,before start training to explore and evaluate your data, use scatter plots to see if indeed its separable, perform feature engineering and preprocessing for this ask yourself: given this features, would a human expert be able to perform predictions?, if your answer is not, transform or drop features so that the answer is positive
I have recently watched a video explaining that for Deep Learning, if you add more data, you don't need as much regularization, which sort of makes sense.
This being said, does this statement hold for "normal" Machine Learning algorithms like Random Forest for example ? And if so, when searching for the best hyper-parameters for the algorithm, in theory you should have as input dataset ( of course that gets further divided into cross validation sets etc ) as much data as you have, and not just a sample of it. This of course means a muuch longer training time, as for every combination of hyper-params you have X cross-validation sets which need to be trained and so on.
So basically, is it fair to assume that the params found for a decently size sample of your dataset are the "best" ones to use for the entire dataset or isn't it ?
Speaking from a statistician's point of view: it really depends on the quality of your estimator. If it's unbiased and low-variance, then a sample will be fine. If the variance is high, you'll want to use all the data you can.
I've got a problem where I've potentially got a huge number of features. Essentially a mountain of data points (for discussion let's say it's in the millions of features). I don't know what data points are useful and what are irrelevant to a given outcome (I guess 1% are relevant and 99% are irrelevant).
I do have the data points and the final outcome (a binary result). I'm interested in reducing the feature set so that I can identify the most useful set of data points to collect to train future classification algorithms.
My current data set is huge, and I can't generate as many training examples with the mountain of data as I could if I were to identify the relevant features, cut down how many data points I collect, and increase the number of training examples. I expect that I would get better classifiers with more training examples given fewer feature data points (while maintaining the relevant ones).
What machine learning algorithms should I focus on to, first,
identify the features that are relevant to the outcome?
From some reading I've done it seems like SVM provides weighting per feature that I can use to identify the most highly scored features. Can anyone confirm this? Expand on the explanation? Or should I be thinking along another line?
Feature weights in a linear model (logistic regression, naive Bayes, etc) can be thought of as measures of importance, provided your features are all on the same scale.
Your model can be combined with a regularizer for learning that penalises certain kinds of feature vectors (essentially folding feature selection into the classification problem). L1 regularized logistic regression sounds like it would be perfect for what you want.
Maybe you can use PCA or Maximum entropy algorithm in order to reduce the data set...
You can go for Chi-Square tests or Entropy depending on your data type. Supervized discretization highly reduces the size of your data in a smart way (take a look into Recursive Minimal Entropy Partitioning algorithm proposed by Fayyad & Irani).
If you work in R, the SIS package has a function that will do this for you.
If you want to do things the hard way, what you want to do is feature screening, a massive preliminary dimension reduction before you do feature selection and model selection from a sane-sized set of features. Figuring out what is the sane-size can be tricky, and I don't have a magic answer for that, but you can prioritize what order you'd want to include the features by
1) for each feature, split the data in two groups by the binary response
2) find the Komogorov-Smirnov statistic comparing the two sets
The features with the highest KS statistic are most useful in modeling.
There's a paper "out there" titled "A selctive overview of feature screening for ultrahigh-dimensional data" by Liu, Zhong, and Li, I'm sure a free copy is floating around the web somewhere.
4 years later I'm now halfway through a PhD in this field and I want to add that the definition of a feature is not always simple. In the case that your features are a single column in your dataset, the answers here apply quite well.
However, take the case of an image being processed by a convolutional neural network, for example, a feature is not one pixel of the input, rather it's much more conceptual than that. Here's a nice discussion for the case of images:
https://medium.com/#ageitgey/machine-learning-is-fun-part-3-deep-learning-and-convolutional-neural-networks-f40359318721
I am new in machine learning. My problem is to make a machine to select a university for the student according to his location and area of interest. i.e it should select the university in the same city as in the address of the student. I am confused in selection of the algorithm can I use Perceptron algorithm for this task.
There are no hard rules as to which machine learning algorithm is the best for which task. Your best bet is to try several and see which one achieves the best results. You can use the Weka toolkit, which implements a lot of different machine learning algorithms. And yes, you can use the perceptron algorithm for your problem -- but that is not to say that you would achieve good results with it.
From your description it sounds like the problem you're trying to solve doesn't really require machine learning. If all you want to do is match a student with the closest university that offers a course in the student's area of interest, you can do this without any learning.
I second the first remark that you probably don't need machine learning if the student has to live in the same area as the university. If you want to use an ML algorithm, maybe it would best to think about what data you would have to start with. The thing that comes to mind is a vector for a university that has certain subjects/areas for each feature. Then compute a distance from a vector which is like an ideal feature vector for the student. Minimize this distance.
The first and formost thing you need is a labeled dataset.
It sounds like the problem could be decomposed into a ML problem however you first need a set of positive and negative examples to train from.
How big is your dataset? What features do you have available? Once you answer these questions you can select an algorithm that bests fits the features of your data.
I would suggest using decision trees for this problem which resembles a set of if else rules. You can just take the location and area of interest of the student as conditions of if and else if statements and then suggest a university for him. Since its a direct mapping of inputs to outputs, rule based solution would work and there is no learning required here.
Maybe you can use a "recommender system"or a clustering approach , you can investigate more deeply the techniques like "collaborative filtering"(recommender system) or k-means(clustering) but again, as some people said, first you need data to learn from, and maybe your problem can be solved without ML.
Well, there is no straightforward and sure-shot answer to this question. The answer depends on many factors like the problem statement and the kind of output you want, type and size of the data, the available computational time, number of features, and observations in the data, to name a few.
Size of the training data
Accuracy and/or Interpretability of the output
Accuracy of a model means that the function predicts a response value for a given observation, which is close to the true response value for that observation. A highly interpretable algorithm (restrictive models like Linear Regression) means that one can easily understand how any individual predictor is associated with the response while the flexible models give higher accuracy at the cost of low interpretability.
Speed or Training time
Higher accuracy typically means higher training time. Also, algorithms require more time to train on large training data. In real-world applications, the choice of algorithm is driven by these two factors predominantly.
Algorithms like Naïve Bayes and Linear and Logistic regression are easy to implement and quick to run. Algorithms like SVM, which involve tuning of parameters, Neural networks with high convergence time, and random forests, need a lot of time to train the data.
Linearity
Many algorithms work on the assumption that classes can be separated by a straight line (or its higher-dimensional analog). Examples include logistic regression and support vector machines. Linear regression algorithms assume that data trends follow a straight line. If the data is linear, then these algorithms perform quite good.
Number of features
The dataset may have a large number of features that may not all be relevant and significant. For a certain type of data, such as genetics or textual, the number of features can be very large compared to the number of data points.
How should I approach a situtation when I try to apply some ML algorithm (classification, to be more specific, SVM in particular) over some high dimensional input, and the results I get are not quite satisfactory?
1, 2 or 3 dimensional data can be visualized, along with the algorithm's results, so you can get the hang of what's going on, and have some idea how to aproach the problem. Once the data is over 3 dimensions, other than intuitively playing around with the parameters I am not really sure how to attack it?
What do you do to the data? My answer: nothing. SVMs are designed to handle high-dimensional data. I'm working on a research problem right now that involves supervised classification using SVMs. Along with finding sources on the Internet, I did my own experiments on the impact of dimensionality reduction prior to classification. Preprocessing the features using PCA/LDA did not significantly increase classification accuracy of the SVM.
To me, this totally makes sense from the way SVMs work. Let x be an m-dimensional feature vector. Let y = Ax where y is in R^n and x is in R^m for n < m, i.e., y is x projected onto a space of lower dimension. If the classes Y1 and Y2 are linearly separable in R^n, then the corresponding classes X1 and X2 are linearly separable in R^m. Therefore, the original subspaces should be "at least" as separable as their projections onto lower dimensions, i.e., PCA should not help, in theory.
Here is one discussion that debates the use of PCA before SVM: link
What you can do is change your SVM parameters. For example, with libsvm link, the parameters C and gamma are crucially important to classification success. The libsvm faq, particularly this entry link, contains more helpful tips. Among them:
Scale your features before classification.
Try to obtain balanced classes. If impossible, then penalize one class more than the other. See more references on SVM imbalance.
Check the SVM parameters. Try many combinations to arrive at the best one.
Use the RBF kernel first. It almost always works best (computationally speaking).
Almost forgot... before testing, cross validate!
EDIT: Let me just add this "data point." I recently did another large-scale experiment using the SVM with PCA preprocessing on four exclusive data sets. PCA did not improve the classification results for any choice of reduced dimensionality. The original data with simple diagonal scaling (for each feature, subtract mean and divide by standard deviation) performed better. I'm not making any broad conclusion -- just sharing this one experiment. Maybe on different data, PCA can help.
Some suggestions:
Project data (just for visualization) to a lower-dimensional space (using PCA or MDS or whatever makes sense for your data)
Try to understand why learning fails. Do you think it overfits? Do you think you have enough data? Is it possible there isn't enough information in your features to solve the task you are trying to solve? There are ways to answer each of these questions without visualizing the data.
Also, if you tell us what the task is and what your SVM output is, there may be more specific suggestions people could make.
You can try reducing the dimensionality of the problem by PCA or the similar technique. Beware that PCA has two important points. (1) It assumes that the data it is applied to is normally distributed and (2) the resulting data looses its natural meaning (resulting in a blackbox). If you can live with that, try it.
Another option is to try several parameter selection algorithms. Since SVM's were already mentioned here, you might try the approach of Chang and Li (Feature Ranking Using Linear SVM) in which they used linear SVM to pre-select "interesting features" and then used RBF - based SVM on the selected features. If you are familiar with Orange, a python data mining library, you will be able to code this method in less than an hour. Note that this is a greedy approach which, due to its "greediness" might fail in cases where the input variables are highly correlated. In that case, and if you cannot solve this problem with PCA (see above), you might want to go to heuristic methods, which try to select best possible combinations of predictors. The main pitfall of this kind of approaches is the high potential of overfitting. Make sure you have a bunch "virgin" data that was not seen during the entire process of model building. Test your model on that data only once, after you are sure that the model is ready. If you fail, don't use this data once more to validate another model, you will have to find a new data set. Otherwise you won't be sure that you didn't overfit once more.
List of selected papers on parameter selection:
Feature selection for high-dimensional genomic microarray data
Oh, and one more thing about SVM. SVM is a black box. You better figure out what is the mechanism that generate the data and model the mechanism and not the data. On the other hand, if this would be possible, most probably you wouldn't be here asking this question (and I wouldn't be so bitter about overfitting).
List of selected papers on parameter selection
Feature selection for high-dimensional genomic microarray data
Wrappers for feature subset selection
Parameter selection in particle swarm optimization
I worked in the laboratory that developed this Stochastic method to determine, in silico, the drug like character of molecules
I would approach the problem as follows:
What do you mean by "the results I get are not quite satisfactory"?
If the classification rate on the training data is unsatisfactory, it implies that either
You have outliers in your training data (data that is misclassified). In this case you can try algorithms such as RANSAC to deal with it.
Your model(SVM in this case) is not well suited for this problem. This can be diagnozed by trying other models (adaboost etc.) or adding more parameters to your current model.
The representation of the data is not well suited for your classification task. In this case preprocessing the data with feature selection or dimensionality reduction techniques would help
If the classification rate on the test data is unsatisfactory, it implies that your model overfits the data:
Either your model is too complex(too many parameters) and it needs to be constrained further,
Or you trained it on a training set which is too small and you need more data
Of course it may be a mixture of the above elements. These are all "blind" methods to attack the problem. In order to gain more insight into the problem you may use visualization methods by projecting the data into lower dimensions or look for models which are suited better to the problem domain as you understand it (for example if you know the data is normally distributed you can use GMMs to model the data ...)
If I'm not wrong, you are trying to see which parameters to the SVM gives you the best result. Your problem is model/curve fitting.
I worked on a similar problem couple of years ago. There are tons of libraries and algos to do the same. I used Newton-Raphson's algorithm and a variation of genetic algorithm to fit the curve.
Generate/guess/get the result you are hoping for, through real world experiment (or if you are doing simple classification, just do it yourself). Compare this with the output of your SVM. The algos I mentioned earlier reiterates this process till the result of your model(SVM in this case) somewhat matches the expected values (note that this process would take some time based your problem/data size.. it took about 2 months for me on a 140 node beowulf cluster).
If you choose to go with Newton-Raphson's, this might be a good place to start.