Machine Learning Project
Exploring Recipe Data
- Naive Bayes
- SVM
- Decision Trees
###lr.py Word lemmatization version 1 - better for logistic regression
cleaned up words and tfidf vectorized ingredients from the corpus
benchmark 50-50 split:
- NB .715
- LR .777
- SVC .778
###nb2.py Word lemmatization version 2 - better for NB simple lowercase lemmatization
benchmark 50-50 split:
- NB .739
- LR .774
- SVC .774
###nb.py no lemmatization.
Includes code to do kfold testing. Do not run kfold with gridsearch (slow)
benchmark 50-50 split:
- NB .731
- LR .729
- SVC .772