I am performing a classification problem using 3 different classifiers namely, Decision Tree, Naive Bayes and IBK. I have two data sets which are the same in layout and attribute names but the values in each are different.
Training Set Example;
State
Population
HouseholdIncome
FamilyIncome
perCapInc
NumUnderPov
EducationLevel_1
EducationLevel_2
EducationLevel_3
UnemploymentRate
EmployedRate
ViolentCrimesPerPop
Crime
Rate
8, 0.19, 0.37, 0.39, 0.4, 0.08, 0.1, 0.18, 0.48, 0.27 ,0.68 ,0.2 ,Low
I would like my decision tree to predict using the 12 attributes if the Target Class value is Low, Med or High based on the ViolentCrimesPerPop figure which in this example is 0.2.
My question is.... On my Test set do I just provide more un-seen examples in the same format or should I take away one of the attributes so i can see if it has learnt anything?
It is a good idea to separate your dataset into three separate sets: Training, Testing and Validation.
The training set is used to train each of the models that you are building. This is usually checked for performance using a testing set. As the designer continues to adjust the parameters of their model (for example, pruning options on Decision Trees and k for k-NN or Neural Network parameters), you can see how well the model is performing against the testing set.
Finally, once these parameters have been completed for your model, you can then run these against a validation set to confirm that the model did not over-fit on the testing data (due to parameter adjustments applied to the model itself).
A further discussion of these sets may be found here.
Generally, I have used a data split of 60-20-20, however it is common to use 50-25-25 as well, it really comes down to how much data you have to play with.
I hope this helps!
It is not a good thing to test your classifier over your same training data, because your model has learnt, hopefully, to classify those instances correctly.
The usual set up is to train over the training dataset and then test it over a different dataset (with the same format/structure), to see how it performs.
Related
I have been using gensim's libraries to train a doc2Vec model. After experimenting with different datasets for training, I am fairly confused about what should be an ideal training data size for doc2Vec model?
I will be sharing my understanding here. Please feel free to correct me/suggest changes-
Training on a general purpose dataset- If I want to use a model trained on a general purpose dataset, in a specific use case, I need to train on a lot of data.
Training on the context related dataset- If I want to train it on the data having the same context as my use case, usually the training data size can have a smaller size.
But what are the number of words used for training, in both these cases?
On a general note, we stop training a ML model, when the error graph reaches an "elbow point", where further training won't help significantly in decreasing error. Has any study being done in this direction- where doc2Vec model's training is stopped after reaching an elbow ?
There are no absolute guidelines - it depends a lot on your dataset and specific application goals. There's some discussion of the sizes of datasets used in published Doc2Vec work at:
what is the minimum dataset size needed for good performance with doc2vec?
If your general-purpose corpus doesn't match your domain's vocabulary – including the same words, or using words in the same senses – that's a problem that can't be fixed with just "a lot of data". More data could just 'pull' word contexts and representations more towards generic, rather than domain-specific, values.
You really need to have your own quantitative, automated evaluation/scoring method, so you can measure whether results with your specific data and goals are sufficient, or improving with more data or other training tweaks.
Sometimes parameter tweaks can help get the most out of thin data – in particular, more training iterations or a smaller model (fewer vector-dimensions) can slightly offset some issues with small corpuses, sometimes. But the Word2Vec/Doc2Vec really benefit from lots of subtly-varied, domain-specific data - it's the constant, incremental tug-of-war between all the text-examples during training that helps the final representations settle into a useful constellation-of-arrangements, with the desired relative-distance/relative-direction properties.
I've seen in multiple places that you should disable dropout during validation and testing stages and only keep it during the training phase. Is there a reason why that should happen? I haven't been able to find a good reason for that and was just wondering.
One reason I'm asking is because I trained a model with dropout, and the results turned out well - about 80% accuracy. Then, I went on to validate the model but forgot to set the prob to 1 and the model's accuracy went down to about 70%. Is it supposed to be that drastic? And is it as simple as setting the prob to 1 in each dropout layer?
Thanks in advance!
Dropout is a random process of disabling neurons in a layer with chance p. This will make certain neurons feel they are 'wrong' in each iteration - basically, you are making neurons feel 'wrong' about their output so that they rely less on the outputs of the nodes in the previous layer. This is a method of regularization and reduces overfitting.
However, there are two main reasons you should not use dropout to test data:
Dropout makes neurons output 'wrong' values on purpose
Because you disable neurons randomly, your network will have different outputs every (sequences of) activation. This undermines consistency.
However, you might want to read some more on what validation/testing exactly is:
Training set: a set of examples used for learning: to fit the parameters of the classifier In the MLP case, we would use the training set to find the “optimal” weights with the back-prop rule
Validation set: a set of examples used to tune the parameters of a classifier In the MLP case, we would use the validation set to find the “optimal” number of hidden units or determine a stopping point for the back-propagation algorithm
Test set: a set of examples used only to assess the performance of a fully-trained classifier In the MLP case, we would use the test to estimate the error rate after we have chosen the final model (MLP size and actual weights) After assessing the final model on the test set, YOU MUST NOT tune the model any further!
Why separate test and validation sets? The error rate estimate of the final model on validation data will be biased (smaller than the true error rate) since the validation set is used to select the final model After assessing the final model on the test set, YOU MUST NOT tune the model any further!
source : Introduction to Pattern Analysis,Ricardo Gutierrez-OsunaTexas A&M University, Texas A&M University (answer)
So even for validation, how would you determine which nodes you remove if the nodes have a random probability of being disactivated?
Dropout is a method of making bagging practical for ensembles of very many large neural networks.
Along the same line we may remember that using the following false explanation:
For the new data, we can predict their classes by taking the average of the results from all the learners:
Since N is a constant we can just ignore it and the result remains the same, so we should disable dropout during validation and testing.
The true reason is much more complex. It is because of the weight scaling inference rule:
We can approximate p_{ensemble} by evaluating p(y|x) in one model: the model with all units, but with the weights going out of unit i multiplied by the probability of including unit i. The motivation for this modification is to capture the right expected value of the output from that unit. There is not yet any theoretical argument for the accuracy of this approximate inference rule in deep nonlinear networks, but empirically it performs very well.
When we train the model using dropout(for example for one layer) we zero out some outputs of some neurons and scale the others up by 1/keep_prob to keep the expectation of the layer almost the same as before. In the prediction process, we can use dropout but we can only get different predictions each time because we drop the values out randomly, then we need to run the prediction many times to get the expected output. Such a process is time-consuming so we can remove the dropout and the expectation of the layer remains the same.
Reference:
Difference between Bagging and Boosting?
7.12 of Deep Learning
Simplest reason can be, during prediction(test, validation or after production deployment) you want to use the capability of each and every learned neurons and really don't like to skip some of them randomly.
Thats the only reason we set probability as 1 during testing.
There is a Bayesian technique called Monte Carlo dropout in which the dropout would be not disabled during testing. The model will run several times with the same dropout rate(or in one go as a batch), and the mean(line 6 depicted below) and variance(line 7 depicted below) of the results will be calculated to determine the uncertainty.
Here is Uber's application to quantify uncertainty:
Short answer:
Dropouts to bring down over fitting in the training data. They are used as a regularization parameters. So if you have high variance (i.e. look at the difference between training set and validation set accuracy for this) then use drop out on training data, as it won't be good enough to apply dropout on test and validation data as you haven't been sure about the neurons which are going to shut off hence laying off the importance of random neurons which can be important.
I am new to image classification, currently working on SVM(support Vector Machine) method for classifying four groups of images by multisvm function, my algorithm every time the training and testing data are randomly selected and the performance is varies at every time. Some one suggested to do cross validation i did not understand why we need cross validation and what is the main purpose of this? . My actual data set consist training matrix size 28×40000 and testing matrix size 17×40000. how to do cross validation by this data set help me. thanks in advance .
Cross validation is used to select your model. The out-of-sample error can be estimated from your validation error. As a result, you would like to select the model with the least validation error. Here the model refers to the features you want to use, and of more importance, the gamma and C in your SVM. After cross validation, you will use the selected gamma and C with the least average validation error to train the whole training data.
You may also need to estimate the performance of your features and parameters to avoid both high-bias and high-variance. Whether your model suffers underfitting or overfitting can be observed from both in-sample-error and validation error.
Ideally 10-fold is often used for cross validation.
I'm not familiar with multiSVM but you may want to check out libSVM, it is a popular, free SVM library with support for a number of different programming languages.
Here they describe cross validation briefly. It is a way to avoid over-fitting the model by breaking up the training data into sub groups. In this way you can find a model (defined by a set of parameters) which fits both sub groups optimally.
For example, in the following picture they plot the validation accuracy contours for parameterized gamma and C values which are used to define the model. From this contour plot you can tell that the heuristically optimal values (from those tested) are those that give an accuracy closer to 84 instead of 81.
Refer to this link for more detailed information on cross-validation.
You always need to cross-validate your experiments in order to guarantee a correct scientific approach. For instance, if you don't cross-validate, the results you read (such as accuracy) might be highly biased by your test set. In an extreme case, your training step might have been very weak (in terms of fitting data) and your test step might have been very good. This applies to ALL machine learning and optimization experiments, not only SVMs.
To avoid such problems just divide your initial dataset in two (for instance), then train in the first set and test in the second, and repeat the process invesely, training in the second and testing in the first. This will guarantee that any biases to the data are visible to you. As someone suggested, you can perform this with even further division: 10-fold cross-validation, means dividing your data set in 10 parts, then training in 9 and testing in 1, then repeating the process until you have tested in all parts.
I am trying to solve classification problem using Matlab GPTIPS framework.
I managed to build reasonable data representation and fitness function so far and got an average accuracy per class near 65%.
What I need now is some help with two difficulties:
My data is biased. Basically I am solving binary classification problem and only 20% of data belongs to class 1, while other 80% belong to class 0. I used accuracy of prediction as my fitness function at first, but it was really bad. The best I have now is
Fitness = 0.5*(PositivePredictiveValue + NegativePredictiveValue) - const*ComplexityOfSolution
Please, advize, how can I improve my function to make correction for data bias.
Second problem is overfitting. I divided my data into three parts: training (70%), testing (20%), validation (10%). I train each chromosome on training set, then evaluate it's fitness function on testing set. This routine allows me to reach fitness of 0.82 on my test data for the best individual in population. But same individual's result on validation data is only 60%.
I added validation check for best individual each time before new population is generated. Then I compare fitness on validation set with fitness on test set. If difference is more then 5%, then I increase penalty for solution complexity in my fitness function. But it didn't help.
I could also try to evaluate all individuals with validation set during each generation, and simply remove overfitted ones. But then I don't see any difference between my test and validation data. What else can be done here?
UPDATE:
For my second question I've found great article "Experiments on Controlling Overtting
in Genetic Programming" Along with some article authors' ideas on dealing with overfitting in GP it has impressive review with a lot of references to many different approaches to the issue. Now I have a lot of new ideas I can try for my problem.
Unfortunately, still cant' find anything on selecting a proper fitness function which will take into account unbalanced class proportions in my data.
65% accuracy is very bad when the baseline (classify everything as the class with most samples) would be 80%. You need to achieve at least baseline classification in order to have a better model than the naive one.
I would not penalize complexity. Rather limit the tree size (if possible). You could identify simpler models during the run, like storing a pareto front of models with quality and complexity as its two fitness values.
In HeuristicLab we have integrated GP based classification that can do these things. There are several options: You can choose to use MSE for classification or R2. In the latest trunk build there is also an evaluator to optimize accuracy directly (exactly speaking it optimizes the classification penalties). Optimizing MSE means it assigns each class a value (1, 2, 3,...) and tries to minimize mean squared error from that value. This may not seem optimal at first, but works. Optimizing accuracy directly may lead to faster overfitting. There is also a formula simplifier which allows you to prune and shrink your formula (and view the effects of that).
Also, does it need to be GP? Have you tried Random Forest Classification or Support Vector Machines as well? RF are pretty fast and work pretty well usually.
Beginner on ANNs:
I am implementing a back propagation neural network to predict the price of gold. I know that I have to split my data into training data, selection data and test data.
However I unsure How to go on about using these sets of data. At first I was training the data network with my training set then after it's trained I am getting a number of inputs to my network from the test set and comparing the output.
I'm not sure if I'm doing this right and were does the selection set come in ?
thanks in advance!
The general idea is:
Train the network for a little while on the training set.
Evaluate the network on a second set, often called the validation set. Probably what you're calling the selection set.
Train the network a little more on the training set.
Evaluate the new network on the selection set again.
Which did better, the old network or the new network? If the new network is better, we're still getting some use out of training, so goto 3. If the new network is worse, more training will probably only hurt. Use the previously version of the network, since it did better.
In this way, you can tell when to stop training.
One easy modification to this is to always keep track of the best network seen so far, and we only stop training when we see some number (say, three) of training attempts that do worse in a row.
The third set, the test set, is necessary because the selection set is, if indirectly, involved in the training process. Final evaluation must be done on data that was not used at all during training.
This sort of thing is sufficient for simple experiments, but in general you'll want to use cross-validation to get a better idea of your system's performance.
I wanted to leave a comment just to say that validation sets are a good place for model-dependent hyper-parameter tuning, but I'm new here and hence lack the reputation points to do so. To make this more worthy of a separate posting, I've included an outline of my own train-validate-test process. In practice, my workflow is as follows:
Identify, collect, and clean data. Try to limit complaining during data munging process.
Split data into three sets: training, validation, test.
Establish two "base" models for evaluating more complex models built later on in the process. The first of these models is typically a basic linear/logistic regression using all possible features. The second models uses only the most obviously informative (initial identification of informative features depends on use case, typically involves combination of domain knowledge, basic clustering, simple correlation).
Begin more empirical feature selection (i.e. unsupervised NN, but usually random forest) and prototype a broad range of models using the training set.
Eliminate poorly performing models as well as uninformative features
Compare performance of remaining models against each other and the "base" models, using a modified version of the training set (same data, but sans uninformative features). Toss under-performing models.
Using the validation set, tune the appropriate hyper-parameters for each of the models (either by hand or gridsearch). Further reduce the number of models in consideration, ideally to just 2-3 (excluding base models).
Finally, evaluate model performance (with optimized hyper-parameters) on the test set. Again, compare models among themselves and against the base models. Make final model choice based on a problem-specific appropriate combination of computational complexity/cost, ease of interpretation/transparency/"explainability", and improvement over and/or performance vs base models.