I'm using SVC(kernel="linear", probability=True) in multiclass classification. when I'm using 2/3rd of my data for training purpose, I'm getting ~72%. And when I tried to predict in production, Confidence scores I'm getting are very less. Does training on the total dataset helps to improve confidence scores?
Does training on the total dataset helps to improve confidence scores?
It might. In general, the more data the better. However evaluating performance should be done on data that the model has not seen before. One way to do this is to set aside a part of the data, a test set, as you have done. Another approach is to use cross-validation, see below.
And when I tried to predict in production, Confidence scores I'm getting are very less.
This means that your model does not generalize well. In other words when presented with data it has not seen before the model starts to make more or less random predictions.
To get a better sense of how well your model generalizes you may want to use cross-validation:
from sklearn.model_selection import cross_val_score
clf = SVC()
scores = cross_val_score(clf, X, Y)
This will train and evaluate your classifier on the full dataset using folds of the full data. A fold For each split the classifier is trained and validation on an exclusive subset of the data. For each split the scores result contains the validation score (for SVC, the accuracy). If you need more control over which metrics to evaluate, use the cross_validation function.
to predict in production
In order to improve your model's performance, there are several methods to consider:
Use more training data
Use an ensemble model to reduce prediction variance
Use a different model (algorithm)
Related
I'm trying to train a multilabel text classification model using BERT. Each piece of text can belong to 0 or more of a total of 485 classes. My model consists of a dropout layer and a linear layer added on top of the pooled output from the bert-base-uncased model from Hugging Face. The loss function I'm using is the BCEWithLogitsLoss in PyTorch.
I have millions of labeled observations to train on. But the training data are highly unbalanced, with some labels appearing in less than 10 observations and others appearing in more than 100K observations! I'd like to get a "good" recall.
My first attempt at training without adjusting for data imbalance produced a micro recall rate of 70% (good enough) but a macro recall rate of 45% (not good enough). These numbers indicate that the model isn't performing well on underrepresented classes.
How can I effectively adjust for the data imbalance during training to improve the macro recall rate? I see we can provide label weights to BCEWithLogitsLoss loss function. But given the very high imbalance in my data leading to weights in the range of 1 to 1M, can I actually get the model to converge? My initial experiments show that a weighted loss function is going up and down during training.
Alternatively, is there a better approach than using BERT + dropout + linear layer for this type of task?
In your case it might be helpful to balance the labels in the training data. You have a lot of data, so you could afford to loose a part of it by balancing. But before you do this, I recommend to read this answer about balancing classes in traing data.
If you really only care about recall, you could try to tune your model maximizing recall.
I develpoed a simple CNN model for MNIST dataset and i got 98% validation accuracy. But after saving the model through keras as model.h5 and evaluating the inference of th saved model in another jypyter session, the performance of the model is poor and the predictions are random
What needs to be done to get same accuracy after saving and uploading the model in different jypyter notebook session?
(Consider sharing your code/results so the community can help you better).
I'm assuming you're using Tensorflow/Keras, so model.save('my_model.h5') after your model.fit(...) should save the model, including the trained parameters (but not including the internal optimizer data; i.e gradients, etc..., which shouldn't affect the prediction capabilities of the model).
A number of things could cause a generalization gap like that, but...
Case 1: having a high training/validation accuracy and a low test (prediction) accuracy typically means your model overfit on the given training data.
I suggest adding some regularization to your training phase (dropout layers, cutout augmentation, L1/L2, etc...), a fewer number of epochs or early-stopping, or cross-validation/data reshuffle to cross off the possibility of overfitting.
Case 2: low intrinsic dataset variance, but unless you're using a subset of MNIST, this is unlikely. Make sure you are properly splitting your training/validation/test sets.
Again, it could be a number of issues, but these are the most common cases for low model generalization. Post your code (specifying the architecture, optimizer, hyperparameters, data prepropcessing, and test data used) so the answers can be more relevant to your problem.
I am trying to classify around 400K data with 13 attributes. I have used python sklearn's SVM package, but it didn't work, and then I learned that SVM's are not suitable for large dataset classification. Then I used the (sklearn) ANN using the following MLPClassifier:
MLPClassifier(solver='adam', alpha=1e-5, random_state=1,activation='relu', max_iter=500)
and trained the system using 200K samples, and tested the model on the remaining ones. The classification worked well. However, my concern is that the system is over trained or overfit. Can you please guide me on the number of hidden layers and node sizes to make sure that there is no overfit? (I have learned that the default implementation has 100 hidden neurons. Is it ok to use the default implementation as is?)
To know if your are overfitting you have to compute:
Training set accuracy
Test set accuracy
Once you have calculated this scores, compare it. If training set score is much better than your test set score, then you are overfitting. This means that your model is "memorizing" your data, instead of learning from it to make future predictions.
If you are overfitting with Neuronal Networks you probably have to reduce the number of layers and reduce the number of neurons per layer. There isn't any strict rule that says the number of layer or neurons you need depending on you dataset size. Every dataset can behaves completely different with the same dataset size.
So, to conclude, if you are overfitting, you would have to evaluate your model accuracy using different parameters of layers and number of neurons, and, then, observe with which values you obtain the best results. There are some methods you can use to find the best parameters, is like gridsearchCV.
I have trained a model and it took me quite a while to find the correct hyperparameters.
The model has now been trained for 15h and it seems to to its job quite well.
When I observed the training and validation loss though, the training loss is somewhat higher than the validation loss. (red curve: training, green: validation)
I use dropout to regularize my model and as far as I have understood, droput is is only applied during training which might be the reason.
Now Iam wondering if I have trained a valid model?
It doesn't seem like the model is heavily underfitted?
Thanks in advance for any advice,
cheers,
M
First, check whether you have good data set, i.e., if it is a classification, then get equal number of images for all classes and get it from same source not from different sources. And regularization, dropout are used for overfitting/High variance so don't worry about these.
Then, I think your model is doing good when you trained your model the initial error between them are different but as you increased the epochs then they both got into some steady path. So it is good. And may be reason for this is as I mentioned above or you should try shuffle them then using train_test_split for getting better distribution of training and validation sets.
A plot of learning curves shows a good fit if:
The plot of training loss decreases to a point of stability.
The plot of validation loss decreases to a point of stability and has a small gap with the training loss.
In your case these conditions are satisfied.
Still if you want to deal with High Bias/underfitting then here are few methods:
Train bigger models
Train longer. Use better optimization techniques
Try different Neural Network Architecture and also hyper parameters
And also you can use cross-validation or GridSearchCV for finding better optimizer or hyper parameters but it may take really long because you have to train it on different parameters each time considering your time which is 15 hours then it might be very long but you will find better parameters and then train on it.
Above all I think your model is doing okay.
If your model underfits, its performance will be lower, similar as in the case of overfitting, because actually it can not learn effectively to get the optimal result, i.e the proper function to fit the given distribution. So you have to use less regularization technique e.g. less dropout to get the optimal result.
Furthermore the sampling can also be crucial, because there can be training-validation subsets where your model performs well on validation set and less effective on training set and vice-versa. This is one of the reason why we use crossvalidation and different sampling methods e.g. stratified k-fold.
I am working on a time-series prediction problem using GradientBoostingRegressor, and I think I'm seeing significant overfitting, as evidenced by a significantly better RMSE for training than for prediction. In order to examine this, I'm trying to use sklearn.model_selection.cross_validate, but I'm having problems understanding the result.
First: I was calculating RMSE by fitting to all my training data, then "predicting" the training data outputs using the fitted model and comparing those with the training outputs (the same ones I used for fitting). The RMSE that I observe is the same order of magnitude the predicted values and, more important, it's in the same ballpark as the RMSE I get when I submit my predicted results to Kaggle (although the latter is lower, reflecting overfitting).
Second, I use the same training data, but apply sklearn.model_selection.cross_validate as follows:
cross_validate( predictor, features, targets, cv = 5, scoring = "neg_mean_squared_error" )
I figure the neg_mean_squared_error should be the square of my RMSE. Accounting for that, I still find that the error reported by cross_validate is one or two orders of magnitude smaller than the RMSE I was calculating as described above.
In addition, when I modify my GradientBoostingRegressor max_depth from 3 to 2, which I would expect reduces overfitting and thus should improve the CV error, I find that the opposite is the case.
I'm keenly interested to use Cross Validation so I don't have to validate my hyperparameter choices by using up Kaggle submissions, but given what I've observed, I'm not clear that the results will be understandable or useful.
Can someone explain how I should be using Cross Validation to get meaningful results?
I think there is a conceptual problem here.
If you want to compute the error of a prediction you should not use the training data. As the name says theese type of data are used only in training, for evaluating accuracy scores you ahve to use data that the model has never seen.
About cross-validation I can tell that it's an approach to find the best training/testing set. The process is as follows: you divide your data into n groups and you do various iterating changing the testing group you pick. If you have n groups you will do n iteration and each time the training and testing set will be different. It's more understamdable in the image below.
Basically what you should do it's kile this:
Train the model using months from 0 to 30 (for example)
See the predictions made with months from 31 to 35 as input.
If the input has to be the same lenght divide feature in half (should be 17 months).
I hope I understood correctly, othewise comment.