Accuracy of training according to ML.NET is 97%. But when I'm trying to predict the class it always returns the same value, no matter what input data is provided. And it doesn't make much sense, because it's clearly not 97%, but 0%. So I wanted to ask is it normal or maybe I need to leave it for 10 hours of training so it reaches higher than 97%.
Training data is Parkinson's Disease (PD) classification from kaggle.
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I am training a DNN in MATLAB , while optimizing my network, I am observing a decrement in accuracy while increasing the epochs. Is it possible?
The loss values in on the other hand decreases during training while increasing epochs. Please guide.
tldr; absolutely.
When entire training dataset is seen once by the model (feed forwarded once), it's termed as 1 epoch.
The below graph shows the general behaviour of accuracy with the number of epochs. Training on more number of epochs can result in low accuracy on validation, even though loss will continue to reduce (training accuracy will be high). This is termed as overfitting.
No. of epochs to train also a hyperparameter that needs fine tuning.
It is absolutely possible:
Especially when you are training in batches
When your learning rate is too high
In Python I am working on a binary classification problem of Fraud detection on travel insurance. Here is the characteristic about my dataset:
Contains 40,000 samples with 20 features. After one hot encoding, the number of features is 50(4 numeric, 46 categorical).
Majority unlabeled: out of 40,000 samples, 33,000 samples are unlabeled.
Highly imbalanced: out of 7,000 labeled samples, only 800 samples(11%) are positive(Fraud).
Metrics is precision, recall and F2 score. We focus more on avoiding false positive, therefore high recall is appreciated. As preprocessing I oversampled positive cases using SMOTE-NC, which takes into account categorical variables as well.
After trying several approaches including Semi-Supervised Learning with Self Training and Label Propagation/Label Spreading etc, I achieved high recall score(80% on training, 65-70% on test). However, my precision score shows some trace of overfitting(60-70% on training, 10% on testing). I understand that precision is good on training because it's resampled, and low on test data because it directly reflects the imbalance of the classes in test data. But this precision score is unacceptably low so I want to solve it.
So to simplify the model I am thinking about applying dimensionality reduction. I found a package called prince which comes with FAMD(Factor Analysis for Mixture Data).
Question 1: How I should do normalization, FAMD, k-fold Cross Validation and resampling? Is my approach below correct?
Question 2: The package prince does not have methods such as fit or transform like in Sklearn, so I cannot do the 3rd step described below. Any other good packages to do fitand transform for FAMD? And is there any other good way to reduce dimensionality on this kind of dataset?
My approach:
Make k folds and isolate one of them for validation, use the rest for training
Normalize training data and transform validation data
Fit FAMD on training data, and transform training and test data
Resample only training data using SMOTE-NC
Train whatever model it is, evaluate on validation data
Repeat 2-5 k times and take the average of precision, recall F2 score
*I would also appreciate for any kinds of advices on my overall approach to this problem
Thanks!
I have two learned sklearn.tree.tree.DecisionTreeClassifiers. Both are trained with the same training data. Both learned with different maximum depths for the decision trees. The depth for the decision_tree_model was 6 and the depth for the small_model was 2. Besides the max_depth, no other parameters were specified.
When I want to get the accuracy on the training data of them both like this:
small_model_accuracy = small_model.score(training_data_sparse_matrix, training_data_labels)
decision_tree_model_accuracy = decision_tree_model.score(training_data_sparse_matrix, training_data_labels)
Surprisingly the output is:
small_model accuracy: 0.61170212766
decision_tree_model accuracy: 0.422496238986
How is this even possible? Shouldn't a tree with a higher maximum depth always have a higher accuracy on the training data when learned with the same training data? Is it maybe that score function, which outputs the 1 - accuracy or something?
EDIT:
I just tested it with even higher maximum depth. The value returned becomes even lower. This hints at it being 1 - accuracy or something like that.
EDIT#2:
It seems to be a mistake I made with working with the training data. I thought about the whole thing again and concluded: "Well if the depth is higher, the tree shouldn't be the reason for this. What else is there? The training data itself. But I used the same data! Maybe I did something to the training data in between?"
Then I checked again and there is a difference in how I use the training data. I need to transform it from an SFrame into a scipy matrix (might have to be sparse too). Now I made another accuracy calculation right after fitting the two models. This one results in 61% accuracy for the small_model and 64% accuracy for the decision_tree_model. That's only 3% more and still somewhat surprising, but at least it's possible.
EDIT#3:
The problem is resolved. I handled the training data in a wrong way and that resulted in different fitting.
Here is the plot of accuracy after fixing the mistakes:
This looks correct and would also explain why the assignment creators chose to choose 6 as the maximum depth.
Shouldn't a tree with a higher maximum depth always have a higher
accuracy when learned with the same training data?
No, definitely not always. The problem is you're overfitting your model to your training data in fitting a more complex tree. Hence, the lower score as increase the maximum depth.
I'm relatively new to Matlab ANN Toolbox. I am training the NN with pattern recognition and target matrix of 3x8670 containing 1s and 0s, using one hidden layer, 40 neurons and the rest with default settings. When I get the simulated output for new set of inputs, then the values are around 0 and 1. I then arrange them in descending order and choose a fixed number(which is known to me) out of 8670 observations to be 1 and rest to be zero.
Every time I run the program, the first row of the simulated output always has close to 100% accuracy and the following rows dont exhibit the same kind of accuracy.
Is there a logical explanation in general? I understand that answering this query conclusively might require the understanding of program and problem, but its made of of several functions to clearly explain. Can I make some changes in the training to get consistence output?
If you have any suggestions please share it with me.
Thanks,
Nishant
Your problem statement is not clear for me. For example, what you mean by: "I then arrange them in descending order and choose a fixed number ..."
As I understand, you did not get appropriate output from your NN as compared to the real target. I mean, your output from NN is difference than target. If so, there are different possibilities which should be considered:
How do you divide training/test/validation sets for training phase? The most division should be assigned to training (around 75%) and rest for test/validation.
How is your training data set? Can it support most scenarios as you expected? If your trained data set is not somewhat similar to your test data sets (e.g., you have some new records/samples in the test data set which had not (near) appear in the training phase, it explains as 'outlier' and NN cannot work efficiently with these types of samples, so you need clustering approach not NN classification approach), your results from NN is out-of-range and NN cannot provide ideal accuracy as you need. NN is good for those data set training, where there is no very difference between training and test data sets. Otherwise, NN is not appropriate.
Sometimes you have an appropriate training data set, but the problem is training itself. In this condition, you need other types of NN, because feed-forward NNs such as MLP cannot work with compacted and not well-separated regions of data very well. You need strong function approximation such as RBF and SVM.
I'm implementing a neural network for a supervised classification task in MATLAB.
I have a training set and a test set to evaluate the results.
The problem is that every time I train the network for the same training set I get very different results (sometimes I get a 95% classification accuracy and sometimes like 60%) for the same test set.
Now I know this is because I get different initial weights and I know that I can use 'seed' to set the same initial weights but the question is what does this say about my data and what is the right way to look at this? How do I define the accuracy I'm getting using my designed ANN? Is there a protocol for this (like running the ANN 50 times and get an average accuracy or something)?
Thanks
Make sure your test set is large enough compared to the training set (e.g. 10% of the overall data) and check it regarding diversity. If your test set only covers very specific cases, this could be a reason. Also make sure you always use the same test set. Alternatively you should google the term cross-validation.
Furthermore, observing good training set accuracy while observing bad test set accuracy is a sign for overfitting. Try to apply regularization like a simple L2 weight decay (simply multiply your weight matrices with e.g. 0.999 after each weight update). Depending on your data, Dropout or L1 regularization could also help (especially if you have a lot of redundancies in your input data). Also try to choose a smaller network topology (fewer layers and/or fewer neurons per layer).
To speed up training, you could also try alternative learning algorithms like RPROP+, RPROP- or RMSProp instead of plain backpropagation.
Looks like your ANN is not converging to the optimal set of weights. Without further details of the ANN model, I cannot pinpoint the problem, but I would try increasing the number of iterations.