I have trained LSTM AND decision tree on my data set (type of text classification). I have used K-cross fold validation with k=10.
Decision tree accuracy 61%
LSTM accuracy 90%
Now when I predict on totally unseen data then decision tree predicts more well and good as compared to LSTM.
Why it happens? If LSTM accuracy is more then why decision tree performs more well on unseen data as compare to LSTM?
Your LSTM model may have greater accuracy than a decision tree when training, but the fact that it doesn't generalize well to unseen data, indicates that the LSTM is overfitting to the training data. Try adjusting the train-validation split and batch size to see if that improves your models.
The validation loss during training would indicate which model is better. You can also try using random forests (cluster of decision trees) which has been known to give better results than one decision tree alone
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Why is the accuracy on train dataset not always 100% while we use the same dataset to train the model?
Though tree-based ML algorithms give us 100% accuracy on train dataset many times, but why is this not happening every time. I know this results in overfitting but why not 100% accuracy every time on the dataset using which our model is trained?
My neural network trainign in pytorch is getting very wierd.
I am training a known dataset that came splitted into train and validation.
I'm shuffeling the data during training and do data augmentation on the fly.
I have those results:
Train accuracy start at 80% and increases
Train loss decreases and stays stable
Validation accuracy start at 30% but increases slowly
Validation loss increases
I have the following graphs to show:
How can you explain that the validation loss increases and the validation accuracy increases?
How can be such a big difference of accuracy between validation and training sets? 90% and 40%?
Update:
I balanced the data set.
It is binary classification. It now has now 1700 examples from class 1, 1200 examples from class 2. Total 600 for validation and 2300 for training.
I still see similar behavior:
**Can it be becuase I froze the weights in part of the network?
**Can it be becuase the hyperparametrs like lr?
I found the solution:
I had different data augmentation for training set and validation set. Matching them also increased the validation accuracy!
If the training set is very large in comparison to the validation set, you are more likely to overfit and learn the training data, which would make generalizing the model very difficult. I see your training accuracy is at 0.98 and your validation accuracy increases at a very slow rate, which would imply that you have overfit your training data.
Try reducing the number of samples in your training set to improve how well your model generalizes to unseen data.
Let me answer your 2nd question first. High accuracy on training data and low accuracy on val/test data indicates the model might not generalize well to infer real cases. That is what the validation process is all about. You need to finetune or even rebuild your model.
With regard to the first question, val loss might not necessarily correspond to the val accuracy. The model makes the prediction based on its model, and loss function calculates the difference between probablities of matrix and the target if you are using CrossEntropy function.
Which metrics is better for multi-label classification in Keras: accuracy or categorical_accuracy? Obviously the last activation function is sigmoid and as loss function is binary_crossentropy in this case.
I would not use Accuracy for classification tasks with unbalanced classes.
Especially for multi-label tasks, you probably have most of your labels to be False. That is, each data point can only have a small set of labels compared to the cardinality of all of the possibile labels.
For that reason accuracy is not a good metric, if your model predict all False (sigmoid activation output < 0.5) then you would measure a very high accuracy.
I would analyze either the AUC or recall/precision at each epoch.
Alternatively a multi-label task can be seen as a ranking task (like Recommender Systems) and you could evaluate precision#k or recall#k where k are the top predicted labels.
If your Keras back-end is TensorFlow, check out the full list of supported metrics here: https://www.tensorflow.org/api_docs/python/tf/keras/metrics.
Actually, there is no metric named accuracy in Keras. When you set metrics=['accuray'] in Keras, the correct accuracy metric will be inferred automatically based on the loss function used. As a result, since you have used binary_crossentropy as the loss function, the binary_accuracy will be chosen as the metric.
Now, you should definitely choose binary_accuracy over categorical_accuracy in a multi-label classification task since classes are independent from each other and the prediction for each class should be considered independently of the predictions for other classes.
I'm working on a project with colorectal cancer stage multiclass-classification using Gene Expression Data. My dataset contains 11 Biomarkers. The results from the classification are around 40%. I have tried different models for classification with KNN, SVM, neural network..., and also I have tried algorithms from ensemble machine learning. Has anyone has any idea what can I do with the dataset to improve the results?
To decide what to do next, you will need some metrics:
How well can a team of human experts classify the data?
What is the model accuracy on the training dataset?
What is the model accuracy on the testing dataset?
If the training accuracy is much worse than human experts, you should increase the complexity of the model until the training results approach or exceed human experts. You can do this by increasing the number of input features, choosing a different machine learning model, or increasing the number of layers in the NN. If the training accuracy is poor, you need to improve this first before spending time improving the testing accuracy.
If the training accuracy is good but the testing accuracy is much worse than the training accuracy, you are probably overfitting. Get or create more training data, and use regularization.
I'm using Naive Bayes classifier in Weka on a data set of 7000 instances with 15 attributes. My baseline accuracy is 87.5% using ZeroR. As a part of data preprocessing I normalized the data set with zero mean and unit variance, applied filter to randomize the dataset. I've used training (70%) and testing (30%) sets, as well as 10-fold cross validation on the entire data set, used supervised discretization and attribute selection and the best accuracy of the classifier I got is 93.43%. Is this small improvement in respect to baseline accuracy?