I am hand tagging twitter messages as Positive, Negative, Neutral. I am try to appreciate is there some logic one can use to identify of the training set what proportion of message should be positive / negative and neutral ?
So for e.g. if I am training a Naive Bayes classifier with 1000 twitter messages should the proportion of pos : neg : neutral be 33 % : 33% : 33% or should it be 25 % : 25 % : 50 %
Logically in my head it seems that I i train (i.e. give more samples for neutral) that the system would be better at identifying neutral sentences then whether they are positive or negative - is that true ? or I am missing some theory here ?
Thanks
Rahul
The problem you're referring to is known as the imbalance problem. Many machine learning algorithms perform badly when confronted with imbalanced training data, i.e. when the instances of one class heavily outnumber those of the other class. Read this article to get a good overview of the problem and how to approach it. For techniques like naive bayes or decision trees it is always a good idea to balance your data somehow, e.g. by random oversampling (explained in the references paper). I disagree with mjv's suggestion to have a training set match the proportions in the real world. This may be appropriate in some cases but I'm quite confident it's not in your setting. For a classification problem like the one you describe, the more the sizes of the class sets differ, the more most ML algorithms will have problems discriminating the classes properly. However, you can always use the information about which class is the largest in reality by taking it as a fallback such that when the classifier's confidence for a particular instance is low or this instance couldn't be classified at all, you would assign it the largest class.
One further remark: finding the positivity/negativity/neutrality in Twitter messages seems to me to be a question of degree. As such, it may be viewes as a regression rather than a classification problem, i.e. instead of a three class scheme you perhaps may want calculate a score which tells you how positive/negative the message is.
There are many other factors... but an important one (in determining a suitable ratio and volume of training data) is the expected distribution of each message category (Positive, Neutral, Negative) in the real world. Effectively, a good baseline for the training set (and the control set) is
[qualitatively] as representative as possible of the whole "population"
[quantitatively] big enough that measurements made from such sets is statistically significant.
The effect of the [relative] abundance of a certain category of messages in the training set is hard to determine; it is in any case a lesser factor -or rather one that is highly sensitive to- other factors. Improvements in the accuracy of the classifier, as a whole, or with regards to a particular category, is typically tied more to the specific implementation of the classifier (eg. is it Bayesian, what are the tokens, are noise token eliminated, is proximity a factor, are we using bi-grams etc...) than to purely quantitative characteristics of the training set.
While the above is generally factual but moderately helpful for the selection of the training set's size and composition, there are ways of determining, post facto, when an adequate size and composition of training data has been supplied.
One way to achieve this is to introduce a control set, i.e. one manually labeled but that is not part of the training set and to measure for different test runs with various subsets of the training set, the recall and precision obtained for each category (or some similar accuracy measurements), for this the classification of the control set. When these measurements do not improve or degrade, beyond what's statistically representative, the size and composition of the training [sub-]set is probably the right one (unless it is an over-fitting set :-(, but that's another issue altogether... )
This approach, implies that one uses a training set that could be 3 to 5 times the size of the training subset effectively needed, so that one can build, randomly (within each category), many different subsets for the various tests.
Related
I’m trying to check the performance of my LDA model using a confusion matrix but I have no clue what to do. I’m hoping someone can maybe just point my in the right direction.
So I ran an LDA model on a corpus filled with short documents. I then calculated the average vector of each document and then proceeded with calculating cosine similarities.
How would I now get a confusion matrix? Please note that I am very new to the world of NLP. If there is some other/better way of checking the performance of this model please let me know.
What is your model supposed to be doing? And how is it testable?
In your question you haven't described your testable assessment of the model the results of which would be represented in a confusion matrix.
A confusion matrix helps you represent and explore the different types of "accuracy" of a predictive system such as a classifier. It requires your system to make a choice (e.g. yes/no, or multi-label classifier) and you must use known test data to be able to score it against how the system should have chosen. Then you count these results in the matrix as one of the combination of possibilities, e.g. for binary choices there's two wrong and two correct.
For example, if your cosine similarities are trying to predict if a document is in the same "category" as another, and you do know the real answers, then you can score them all as to whether they were predicted correctly or wrongly.
The four possibilities for a binary choice are:
Positive prediction vs. positive actual = True Positive (correct)
Negative prediction vs. negative actual = True Negative (correct)
Positive prediction vs. negative actual = False Positive (wrong)
Negative prediction vs. positive actual = False Negative (wrong)
It's more complicated in a multi-label system as there are more combinations, but the correct/wrong outcome is similar.
About "accuracy".
There are many kinds of ways to measure how well the system performs, so it's worth reading up on this before choosing the way to score the system. The term "accuracy" means something specific in this field, and is sometimes confused with the general usage of the word.
How you would use a confusion matrix.
The confusion matrix sums (of total TP, FP, TN, FN) can fed into some simple equations which give you, these performance ratings (which are referred to by different names in different fields):
sensitivity, d' (dee-prime), recall, hit rate, or true positive rate (TPR)
specificity, selectivity or true negative rate (TNR)
precision or positive predictive value (PPV)
negative predictive value (NPV)
miss rate or false negative rate (FNR)
fall-out or false positive rate (FPR)
false discovery rate (FDR)
false omission rate (FOR)
Accuracy
F Score
So you can see that Accuracy is a specific thing, but it may not be what you think of when you say "accuracy"! The last two are more complex combinations of measure. The F Score is perhaps the most robust of these, as it's tuneable to represent your requirements by combining a mix of other metrics.
I found this wikipedia article most useful and helped understand why sometimes is best to choose one metric over the other for your application (e.g. whether missing trues is worse than missing falses). There are a group of linked articles on the same topic, from different perspectives e.g. this one about search.
This is a simpler reference I found myself returning to: http://www2.cs.uregina.ca/~dbd/cs831/notes/confusion_matrix/confusion_matrix.html
This is about sensitivity, more from a science statistical view with links to ROC charts which are related to confusion matrices, and also useful for visualising and assessing performance: https://en.wikipedia.org/wiki/Sensitivity_index
This article is more specific to using these in machine learning, and goes into more detail: https://www.cs.cornell.edu/courses/cs578/2003fa/performance_measures.pdf
So in summary confusion matrices are one of many tools to assess the performance of a system, but you need to define the right measure first.
Real world example
I worked through this process recently in a project I worked on where the point was to find all of few relevant documents from a large set (using cosine distances like yours). This was like a recommendation engine driven by manual labelling rather than an initial search query.
I drew up a list of goals with a stakeholder in their own terms from the project domain perspective, then tried to translate or map these goals into performance metrics and statistical terms. You can see it's not just a simple choice! The hugely imbalanced nature of our data set skewed the choice of metric as some assume balanced data or else they will give you misleading results.
Hopefully this example will help you move forward.
I am trying to build a model on a class imbalanced dataset (binary - 1's:25% and 0's 75%). Tried with Classification algorithms and ensemble techniques. I am bit confused on below two concepts as i am more interested in predicting more 1's.
1. Should i give preference to Sensitivity or Positive Predicted Value.
Some ensemble techniques give maximum 45% of sensitivity and low Positive Predicted Value.
And some give 62% of Positive Predicted Value and low Sensitivity.
2. My dataset has around 450K observations and 250 features.
After power test i took 10K observations by Simple random sampling. While selecting
variable importance using ensemble technique's the features
are different compared to the features when i tried with 150K observations.
Now with my intuition and domain knowledge i felt features that came up as important in
150K observation sample are more relevant. what is the best practice?
3. Last, can i use the variable importance generated by RF in other ensemple
techniques to predict the accuracy?
Can you please help me out as am bit confused on which w
The preference between Sensitivity and Positive Predictive value depends on your ultimate goal of the analysis. The difference between these two values is nicely explained here: https://onlinecourses.science.psu.edu/stat507/node/71/
Altogether, these are two measures that look at the results from two different perspectives. Sensitivity gives you a probability that a test will find a "condition" among those you have it. Positive Predictive value looks at the prevalence of the "condition" among those who is being tested.
Accuracy is depends on the outcome of your classification: it is defined as (true positive + true negative)/(total), not variable importance's generated by RF.
Also, it is possible to compensate for the imbalances in the dataset, see https://stats.stackexchange.com/questions/264798/random-forest-unbalanced-dataset-for-training-test
I have a multi-class text classification/categorization problem. I have a set of ground truth data with K different mutually exclusive classes. This is an unbalanced problem in two respects. First, some classes are a lot more frequent than others. Second, some classes are of more interest to us than others (those generally positively correlate with their relative frequency, although there are some classes of interest that are fairly rare).
My goal is to develop a single classifier or a collection of them to be able to classify the k << K classes of interest with high precision (at least 80%) while maintaining reasonable recall (what's "reasonable" is a bit vague).
Features that I use are mostly typical unigram-/bigram-based ones plus some binary features coming from metadata of the incoming documents that are being classified (e.g. whether them were submitted via email or though a webform).
Because of the unbalanced data, I am leaning toward developing binary classifiers for each of the important classes, instead of a single one like a multi-class SVM.
What ML learning algorithms (binary or not) implemented in scikit-learn allow for training tuned to precision (versus for example recall or F1) and what options do I need to set for that?
What data analysis tools in scikit-learn can be used for feature selection to narrow down the features that might be the most relevant to the precision-oriented classification of a particular class?
This is not really a "big data" problem: K is about 100, k is about 15, the total number of samples available to me for training and testing is about 100,000.
Thx
Given that k is small, I would just do this manually. For each desired class, train your individual (one vs the rest) classifier, take look at the precision-recall curve, and then choose the threshold that gives the desired precision.
I have training data that falls into two classes, let's say Yes and No. The data represents three tasks, easy, medium and difficult. A person performs these tasks and is classified into one of the two classes as a result. Each task is classified independently and then the results are combined. I am using 3 independently trained SVM classifiers and then voting on the final result.
I am looking to provide a measure of confidence or probability associated with each classification. LIBSVM can provide a probability estimate along with the classification for each task (easy, medium and difficult, say Pe, Pm and Pd) but I am unsure of how best to combine these into an overall estimate for the final classification of the person (let's call it Pp).
My attempts so far have been along the lines of a simple average:
Pp = (Pe + Pm + Pd) / 3
An Inverse-variance weighted average (since each task is repeated a few times and sample variance (VARe, VARm and VARd) can be calculated - in which case Pe would be a simple average of all the easy samples):
Pp = (Pe/VARe + Pm/VARm + Pd/VARd) / (( 1/VARe ) + ( 1/VARm ) + ( 1/VARd ))
Or a multiplication (under the assumption that these events are independent, which I am unsure of since the underlying tasks are related):
Pp = Pe * Pm * Pd
The multiplication would provide a very low number, so it's unclear how to interpret that as an overall probability when the results of the voting are very clear.
Would any of these three options be the best or is there some other method / detail I'm overlooking?
Based on your comment, I will make the following suggestion. If you need to do this as an SVM (and because, as you say, you get better performance when you do it this way), take the output from your intermediate classifiers and feed them as features to your final classifier. Even better, switch to a multi-layer Neural Net where your inputs represent inputs to the intermediates, the (first) hidden layer represents outputs to the intermediate problem, and subsequent layer(s) represent the final decision you want. This way you get the benefit of an intermediate layer, but its output is optimised to help with the final prediction rather than for accuracy in its own right (which I assume you don't really care about).
The correct generative model for these tests likely looks something like the following:
Generate an intelligence/competence score i
For each test t: generate pass/fail according to p_t(pass | i)
This is simplified, but I think it should illustrate tht you have a latent variable i on which these tests depend (and there's also structure between them, since presumably p_easy(pass|i) > p_medium(pass|i) > p_hard(pass|i); you could potentially model this as a logistic regression with a continuous 'hardness' feature). I suspect what you're asking about is a way to do inference on some thresholding function of i, but you want to do it in a classification way rather than as a probabilistic model. That's fine, but without explicitly encoding the latent variable and the structure between the tests it's going to be hard (and no average of the probabilities will account for the missing structure).
I hope that helps---if I've made assumptions that aren't justified, please feel free to correct.
I have a dataset which the instances are of about 200 features, about 11 of these features are numerical (integer) and the rest are binary (1/0) , these features may be correlated and they are of different probability distributions ,
It's been a while that I've been for a good similarity score which works for a mixed vector and takes into account the correlation between the features,
Do you know such similarity score?
Thanks,
Arian
In your case, the similarity function relies heavily on the input data patterns. You might benefit from learning a distance metric for the input space of data from a given collection
of pair of similar/dissimilar points that preserves the distance relation among the
training data.
Here is a nice survey paper.
The numerous types of distance measures, Euclidean, Manhattan, etc are going provide different levels of accuracy depending on the dataset. Best to read papers covering your method of data fitting and see what heuristics they use. Not to mention that some methods require only homogeneous data that scale accordingly. Here is a paper that talks about a whole host of measures that you might find attractive.
And as always, test and cross validate to see if there really is an impact from the mixing of feature types.