nbayes | A robust , full-featured Ruby implementation of Naive Bayes
kandi X-RAY | nbayes Summary
kandi X-RAY | nbayes Summary
NBayes is a full-featured, Ruby implementation of Naive Bayes. Some of the features include:. For more information, view this blog post:
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Top functions reviewed by kandi - BETA
- Calculates classification
- Normalize the given value
- Truncate the category of tokens
- Remove all the words of the given word
- Load class instance
- Calls the classification class and returns an array of classes .
- Collects a token into categories .
- new category
- Dump given argument to file
- The total number of tokens of tokens .
nbayes Key Features
nbayes Examples and Code Snippets
Community Discussions
Trending Discussions on nbayes
QUESTION
I'm using the Naive Bayes Classifier from nltk to perform sentiment analysis on some tweets. I'm training the data using the corpus file found here: https://towardsdatascience.com/creating-the-twitter-sentiment-analysis-program-in-python-with-naive-bayes-classification-672e5589a7ed, as well as using the method there.
When creating the training set I've done it using all ~4000 tweets in the data set but I also thought I'd test with a very small amount of 30.
When testing with the entire set, it only returns 'neutral' as the labels when using the classifier on a new set of tweets but when using 30 it will only return positive, does this mean my training data is incomplete or too heavily 'weighted' with neutral entries and is the reason for my classifier only returning neutral when using ~4000 tweets in my training set?
I've included my full code below.
...ANSWER
Answered 2019-May-26 at 22:06When doing machine learning, we want to learn an algorithms that performs well on new (unseen) data. This is called generalization.
The purpose of the test set is, amongst others, to verify the generalization behavior of your classifier. If your model predicts the same labels for each test instance, than we cannot confirm that hypothesis. The test set should be representative of the conditions in which you apply it later.
As a rule of thumb, I like to think that you keep 50-25% of their data as a test set. This of course depends on the situation. 30/4000 is less than one percent.
A second point that comes to mind is that when your classifier is biased towards one class, make sure each class is represented nearly equally in the training and validation set. This prevents the classifier from 'just' learning the distribution of the whole set, instead of learning which features are relevant.
As a final note, normally we report metrics such as precision, recall and Fβ=1 to evaluate our classifier. The code in your sample seems to report something based on the global sentiment in all tweets, are you sure that is what you want? Are the tweets a representative collection?
QUESTION
I followed the tutorial here: https://towardsdatascience.com/creating-the-twitter-sentiment-analysis-program-in-python-with-naive-bayes-classification-672e5589a7ed to create a twitter sentiment analyser, which uses naive bayes classifier from the nltk library as a way to classify tweets as either positive, negative or neutral but the labels it gives back are only neutral or irrelevant. I've included my code below as I'm not very experienced with any machine learning so I'd appreciate any help.
I've tried using different sets of tweets to classify, even when specifying a search keyword like 'happy' it will still return 'neutral'. I don't b
...ANSWER
Answered 2019-May-21 at 07:51Your dataset is highly imbalanced. You yourself mentioned it in one of the comment, you have 550 positive and 550 negative labelled tweets but 4000 neutral that's why it always favours the majority class. You should have equal number of utterances for all classes if possible. You also need to learn about evaluation metrics, then you'll see most probably your recall is not good. An ideal model should stand good on all evaluation metrics. To avoid overfitting some people also add a fourth 'others' class as well but for now you can skip that.
Here's something you can do to improve performance of your model, either (add more data) oversample the minority classes by adding possible similar utterances or undersample the majority class or use a combination of both. You can read about oversampling, undersampling online.
In this new datset try to have utterances of all classes in this ratio 1:1:1 if possible. Finally try other algos as well with hyperparameters tuned through grid search,random search or tpot.
edit: in your case irrelevant is the 'others' class so you now have 4 classes try to have dataset in this ratio 1:1:1:1 for each class.
QUESTION
I am training a Naive Bayes model using the mlr package.
I would like to tune the threshold (and only the threshold) for the classification. The tutorial provides an example for doing this while also doing additional hyperparameter tuning in a nested CV-setting. I actually do not want to tune any other (hyper)parameter while finding the optimal threshold value.
Based on the discussion here I set up a makeTuneWrapper() object and set another parameter (laplace) to a fixed value (1) and subsequently run resample() in a nested CV-setting.
...ANSWER
Answered 2019-Feb-12 at 16:44You can use tuneThreshold() directly:
QUESTION
I am trying to build a Perl module out of a CXX module using Swig. There are multiple guides related to this:
- The generic Swig tutorial with a Perl section
- The Swig and C++ guide
- The Swig and Perl5 guide
I'm new to Swig and not very familiar with C(++), but I've been able to compile my module following the tutorial in 1:
I created an interface file:
...ANSWER
Answered 2018-Jul-18 at 01:28Can't load './my_module.so' for module my_module: ./my_module.so: wrong ELF class: ELFCLASS64
QUESTION
I will add a wordInDoc object (word: num) if the word is in the object vocab [positive], I try with equal to but fail. Why?
this is my code
...ANSWER
Answered 2017-May-03 at 11:42Is this a solution you were looking for?
Loop through the array 'docs' then check for the index of matching in 'vocab[_class][wd]'.
Some other validation should be done for non existent classes'_class'.
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Install nbayes
On a UNIX-like operating system, using your system’s package manager is easiest. However, the packaged Ruby version may not be the newest one. There is also an installer for Windows. Managers help you to switch between multiple Ruby versions on your system. Installers can be used to install a specific or multiple Ruby versions. Please refer ruby-lang.org for more information.
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