Comments (1)
I fixed it by passing the classifiers as individual objects rather than as a list of classifiers
from naturallanguage.
Related Issues (20)
- Make Path names of writeAlgoEvaluation() relative and write to their own directory
- Verify that the Part of Speech Is working properly HOT 1
- POS tag is broken HOT 1
- Why does Most Informative Features not limit itself to only word_features HOT 1
- Train a new Alog on the new 1.6 million Tweets HOT 2
- Write a method to convert a tweet to a boolean vector given a word_feature list HOT 1
- Figure out how to see the accuracy for the train_on_kaggle() HOT 1
- Write a method that takes in a tweet and spits out a prediction. HOT 1
- Create a confusion matrix to see true/false positives and negatives
- Figure out why the vote_classifier is labeling everything as postive HOT 1
- Scrape 10k english tweets to a csv file HOT 1
- Retrain the classifiers using Positive, Neutral and Negative sentiment
- Consider using A Naive Bayes Classifier instead of a SGDClassifier
- Test Vector to Text and Text to Vector in dataLabeling.py HOT 1
- What is the correlation between the different version of the SGDClassifiers
- Figure out the influence of Batch size on the SGD models
- Create a manual list of words to exclude from the word_features. HOT 1
- Write a method to remove error causing characters from tweets before classifying them
- Consider only treating a word as a feature when the weight it >.5 or some threshold
- Rewrite feature_sets to be a numpy array. HOT 1
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from naturallanguage.