4 ms·
This post is a great word2vec/keras intro, but it doesn't do one thing you should _always_ do before you break out the neural networks: try to solve the problem
by williamsmj 9y ago
This post is a great word2vec/keras intro, but it doesn't do one thing you should _always_ do before you break out the neural networks: try to solve the problem with traditional machine learning to establish a performance baseline and confirm the data is predictive.
78.9% accuracy on a sentiment classification of tweets with no neutral class is actually slightly _worse_ than you get if you do this in scikit-learn with plain old bag of words and Logistic Regression: https://github.com/williamsmj/sentiment/blob/master/sentiment.ipynb https://github.com/williamsmj/sentiment/blob/master/sentimen....
- ahmedbesbes 9y agoYes, I'm totally aware of that. I'm currently working on a slightly modified version of the code that will include CNN and hopefully better accuracy.
- throwaway_bob 9y agoI think you are missing the parent's point. A CNN is a more complicated model, not a simpler one- it is better to try simple linear classifiers on bags of word or bags of bigrams or trigrams before breaking out the more complicated neural models. Note that you can do this easily with VW or FastText.
- ahmedbesbes 9y agothe aim of my post is not to make a prediction based on a standard alogrithm. my goal is to show that DL techniques make better predictions. What I wrote was a first draft of the algorithm, and I've not made my point yet, unfortunately. I'm working now on a better solution. Could suggest improving the DL algorithm I wrote? Thank you.
- williamsmj 9y agoAgain, you're missing the point. If your goal is to show that DL techniques make better predictions, you need to begin by answering the question "better than what?"
- deleted 9y ago[deleted]