I have a dataset of people's medical records which contains people of different races, income levels, genders, etc.
I have a binary classification problem, and want to compare the accuracy of the two models:
Model 1: I have trained and tested a classifier on a random sample from the population.
Model 2: I have trained and tested a classifier on a sample of the population where the income level is above say 100k.
The test set sizes are of different sizes (both contain over 50,000 people). The test sets may contain some of the same people of course - I have a lot of data and will be doing the above experiment for many different conditions so I am not mentioning how many people overlap between the sets as it will change depending on the condition.
I believe I can't use a standard or modified t-test to compare the performance on the separate test sets, since the demographics are different in the two test sets - is this correct?
Instead, I think the only option is to compare the accuracy of model 1 on the same test as model 2 to figure out if model 2 performs better?
Related
I am a student and working on my first simple machine learning project. The project is about classifying articles into fake and true. I want to use SVM as classification algorithm and two different types of features:
TF-IDF
Lexical Features like the count of exclamation marks and numbers
I have figured out how to use the lexical features and TF-IDF as a features separately. However, I have not managed to figure out, how to combine them.
Is it possible, to train and test two separate learning algorithms (one with TF-IDF and the other one with lexical features) and later combine the results?
For example, can I calculate Accuracy, Precision and Recall for both separately and then take the average?
One way of combining two models is called model stacking. The idea behind it is, that you take the predictions of both models and feed them into a third model (called meta-model) which is then trained to make predictions given the output of the first two models. There is also another version of model stacking where you aditionally feed the original features into the meta-model.
However, in your case another way to combine both approaches would be to simply feed both the TF-IDF and the lexical features into one model and see how that performs.
For example, can I calculate Accuracy, Precision and Recall for both separately and then take the average?
This would unfortunately not work, because there is no combined model making those predictions for which your calculated metrics would be true.
Is there a limit on how many times one can train their CNN model? In the sense, say I have my CNN model and a training set.I train my model and using a unseen data test it. If I am not satisfied with the test accuracy, can I retrain my CNN as many times as possible (hypothetically) and test it again, till the performance is better?
I know other ways to improve the performance like, changing the structure of the network, filter size and number of filters, but say I want to have the structure and hyper-parameters fixed. Also I see when I train my CNN for the fifth or sixth time it gives me better test accuracy.
Is this correct?
Thanks for your time and help.
--Venkat
There is no limit on the number of times one can train a neural network, but the important thing would be to save the weights of your model after some iterations so that you can reload it whenever you want and continue from wherever the training left. This would help you in saving time as well as compute. Number of iterations required for a neural network varies from data to data and architecture to architecture, Ideally shallow models would need less iterations and deeper models would need more. I have both worked on models producing good results in a single iterations as well as models converging after fifty iterations.
I have a question regarding cross validation in Linear regression model.
From my understanding, in cross validation, we split the data into (say) 10 folds and train the data from 9 folds and the remaining folds we use for testing. We repeat this process until we test all of the folds, so that every folds are tested exactly once.
When we are training the model from 9 folds, should we not get a different model (may be slightly different from the model that we have created when using the whole dataset)? I know that we take an average of all the "n" performances.
But, what about the model? Shouldn't the resulting model also be taken as the average of all the "n" models? I see that the resulting model is same as the model which we created using whole of the dataset before cross-validation. If we are considering the overall model even after cross-validation (and not taking avg of all the models), then what's the point of calculating average performance from n different models (because they are trained from different folds of data and are supposed to be different, right?)
I apologize if my question is not clear or too funny.
Thanks for reading, though!
I think that there is some confusion in some of the answers proposed because of the use of the word "model" in the question asked. If I am guessing correctly, you are referring to the fact that in K-fold cross-validation we learn K-different predictors (or decision functions), which you call "model" (this is a bad idea because in machine learning we also do model selection which is choosing between families of predictors and this is something which can be done using cross-validation). Cross-validation is typically used for hyperparameter selection or to choose between different algorithms or different families of predictors. Once these chosen, the most common approach is to relearn a predictor with the selected hyperparameter and algorithm from all the data.
However, if the loss function which is optimized is convex with respect to the predictor, than it is possible to simply average the different predictors obtained from each fold.
This is because for a convex risk, the risk of the average of the predictor is always smaller than the average of the individual risks.
The PROs and CONs of averaging (vs retraining) are as follows
PROs: (1) In each fold, the evaluation that you made on the held out set gives you an unbiased estimate of the risk for those very predictors that you have obtained, and for these estimates the only source of uncertainty is due to the estimate of the empirical risk (the average of the loss function) on the held out data.
This should be contrasted with the logic which is used when you are retraining and which is that the cross-validation risk is an estimate of the "expected value of the risk of a given learning algorithm" (and not of a given predictor) so that if you relearn from data from the same distribution, you should have in average the same level of performance. But note that this is in average and when retraining from the whole data this could go up or down. In other words, there is an additional source of uncertainty due to the fact that you will retrain.
(2) The hyperparameters have been selected exactly for the number of datapoints that you used in each fold to learn. If you relearn from the whole dataset, the optimal value of the hyperparameter is in theory and in practice not the same anymore, and so in the idea of retraining, you really cross your fingers and hope that the hyperparameters that you have chosen are still fine for your larger dataset.
If you used leave-one-out, there is obviously no concern there, and if the number of data point is large with 10 fold-CV you should be fine. But if you are learning from 25 data points with 5 fold CV, the hyperparameters for 20 points are not really the same as for 25 points...
CONs: Well, intuitively you don't benefit from training with all the data at once
There are unfortunately very little thorough theory on this but the following two papers especially the second paper consider precisely the averaging or aggregation of the predictors from K-fold CV.
Jung, Y. (2016). Efficient Tuning Parameter Selection by Cross-Validated Score in High Dimensional Models. International Journal of Mathematical and Computational Sciences, 10(1), 19-25.
Maillard, G., Arlot, S., & Lerasle, M. (2019). Aggregated Hold-Out. arXiv preprint arXiv:1909.04890.
The answer is simple: you use the process of (repeated) cross validation (CV) to obtain a relatively stable performance estimate for a model instead of improving it.
Think of trying out different model types and parametrizations which are differently well suited for your problem. Using CV you obtain many different estimates on how each model type and parametrization would perform on unseen data. From those results you usually choose one well suited model type + parametrization which you will use, then train it again on all (training) data. The reason for doing this many times (different partitions with repeats, each using different partition splits) is to get a stable estimation of the performance - which will enable you to e.g. look at the mean/median performance and its spread (would give you information about how well the model usually performs and how likely it is to be lucky/unlucky and get better/worse results instead).
Two more things:
Usually, using CV will improve your results in the end - simply because you take a model that is better suited for the job.
You mentioned taking the "average" model. This actually exists as "model averaging", where you average the results of multiple, possibly differently trained models to obtain a single result. Its one way to use an ensemble of models instead of a single one. But also for those you want to use CV in the end for choosing reasonable model.
I like your thinking. I think you have just accidentally discovered Random Forest:
https://en.wikipedia.org/wiki/Random_forest
Without repeated cv your seemingly best model is likely to be only a mediocre model when you score it on new data...
I am trying different classifiers of Weka on my data set. I have small dataset and I am classifying my data into five classes.
My problem is that when I apply cross validation or percentage split classification by different classifiers, I get very different results.
For example, when I use NaiveBayse or BayseNet classifiers, I have an F-score of around 40 for all classes, but using SMO I get an F-score of 20. The worse result is obtained when I use LibLinear classifier which gives me a F-scores of around 15.
Maybe I should mention that since LibLinear classifier doesn't accept nominals, I assign a code to each of the possible nominal values and use them as Numeric values in my dataset.
Can anybody tell me why I get such different results? I expected all classifiers to have roughly similar results.
In addition, when I use LibLinear on my test set, I have all data classified under one of the classes and there is no instances in the other four classes.
Thanks in advance,
Why would you expect similar results? For small data set especially I think different methods could easily lead to different predictions. Also linear model has tolerance threshold that would cause early termination before convergence. It's something you can play with in LibLINEAR or SMO for instance.
I am trying to classify the four groups of images using SVM method, by randomly selecting training and testing data each time. When T run the program the performance varies due to randomly selecting data. How to get accurate performance of my algorithm and also how to calculate training and testing accuracy?
The formula I am using for performance is
Performance = sum(PredictedLabels == test_labels) / numel(PredictedLabels)
I am using multisvm function for classification.
My suggestion:
Actually the performance measure is acceptable, though there are some other slightly better choices like #Dan has mentioned.
More importantly, you need to deal with randomness.
1) Everytime you select your training data, test the trained model with multiple randomized test data and average the accuracy. (e.g. 10 times or so)
2) Use multiple trained model and average the performance to get general performance.
Remark:
1) You need to make sure the training data and test data do not overlap. Or it is no longer test data.
2) It is better to have the training data have the same number of samples from each class label. This means you can partition your dataset in advance.