I am struggling to come up with a saliency map that can look at my structured data and give me a saliency map. I am doing this with scikit learn and python.
Especially when I am trying to see what features affect the output the most for random instances in my data.
Everything I look up online is for images only and it is difficult for me to translate over since I am a beginner. My professor says that this is what I have to do but I nothing but confused.
I have tried looking at code on other websites relating to saliency.
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This is the closest thing I could find to help me but I am struggling. My data has various features and a single output.
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I was going through the tutorial of speech emotion recognition and in between saw an "MLPClassifier(Multilayer_perceptron)" which was imported from the sklearn. And there are lots of other like Random forest and linear Regression, standardscalar, GridSearchCV, etc. I was searching for tutorials or steps to how can I create these types of classifiers or modules on my own?
When I searched regarding these, I was getting examples of tutorials of the use cases of predefined classifiers of sklearn and third party claassifiers. Like above specified.
If you guys know any tutorial or steps to achieve these please suggest to me.
Fro MLP, The implementation is quite easy, there is good explanation on how to implement on the Coursera's ML introdcution look to week 4 and week 5, for Linear and logistic regression look to week 2 and week 3. Look at this link for implementing CART, random forests are quite similar I think you can figure out how to implement them easily if you are able to implement CART. For SVM and kernel methods you can look to this repo
i been trying to learn a bit of machine learning for a project that I'm working in. At the moment I managed to classify text using SVM with sklearn and spacy having some good results, but i want to not only classify the text with svm, I also want it to be classified based on a list of keywords that I have. For example: If the sentence has the word fast or seconds I would like it to be classified as performance.
I'm really new to machine learning and I would really appreciate any advice.
I assume that you are already taking a portion of your data, classifying it manually and then using the result as your training data for the SVM algorithm.
If yes, then you could just append your list of keywords (features) and desired classifications (labels) to your training data. If you are not doing it already, I'd recommend using the SnowballStemmer on your training data features.
Good day, I am a student that is interested in NLP. I have come across the demo on AllenNLP's homepage, which stated that:
The model is a simple LSTM using GloVe embeddings that is trained on the binary classification setting of the Stanford Sentiment Treebank. It achieves about 87% accuracy on the test set.
Is there any reference to the sample code or any tutorial that I can follow to replicate this result, so that I can learn more about this subject? I am trying to obtain a Regression Output (Instead of classification).
I hope that someone can point me in the right direction.. Any help is much appreciated. Thank you!
AllenAI provides all code for examples and lib opensource on Git, including AllenNLP.
I found exactly how the example was run here: https://github.com/allenai/allennlp/blob/master/allennlp/tests/data/dataset_readers/stanford_sentiment_tree_bank_test.py
However, to make it a Regression task, you'll have to tweak directly on Pytorch, which is the underlying technology for AllenNLP.
I'm new in the field of Deep Neural Network. There are various deep learning frameworks nearby. Notably Theano, Torch7, Caffe, and recently open sourced TensorFlow. I have tried out a couple of tutorials with TensorFlow provided on their site. Specifically the MNIST dataset. I guess this is the hello world of every deep learning framework out there. I also viewed tutorials from here. This one was explained in detail, but they do not provide hands on experience with any deep learning frameworks. So which framework should be better for beginners? I looked up similar questions asked on Quora. Some said that theano is tougher to learn but it gives more control, Caffe is easier, but it gives less control over the network. And nothing on Tensorflow, as it is new, but from what i've seen the documentation is not That well written, also it seems tougher to understand. So as a newbie what should i choose to learn?
Another question, As I said, MNIST is the hello world of every deep learning framework, and many neural networks can be found for recognizing MNIST dataset. So, if I use the same network to detect other dataset, say CIFAR-10 dataset, will it work?? Let's just say that i turn the CIFAR-10 dataset to grayscale images and convert it to same dimension as MNIST dataset. Will the model be invalid or fail to learn? or have bad accuracy or what?
I am trying to build a classifier to detect subjectivity. I have text files tagged with subjective and objective . I am little lost with the concept of features creation from this data. I have found the lexicon of the subjective and objective tag. One thing that I can do is to create a feature of having words present in respective dictionary. Maybe the count of words present in subjective and objective dictionary. After that I intend to use naive bayes or SVM to develop the model
My problem is as follow
Is my approach correct ?
Can I create more features ? If possible suggest some or point me to some paper or link
Can I do some test like chi -sq etc to identify effective words from the dictionary ?
You are basically on the right track. I would try and apply classifier with features you already have and see how well it will work, before doing anything else.
Actually best way to improve your work is to google for subjectivity classification papers and read them (there are a quite a number of them). For example this one lists typical features for this task.
And yes Chi-squared can be used to construct dictionaries for text classification (other commonly used methods are TD*IDF, pointwise mutal information and LDA)
Also, recently new neural network-based methods for text classification such as paragraph vector and dynamic convolutional neural networks with k-max pooling demonstrated state-of-the-art results on sentiment analysis, thus they should probably be good for subjectivity classification as well.