Mini Project: Naive Bayes
https://machinelearningmastery.com/overfitting-and-underfitting-with-machine-learning-algorithms/
http://adataanalyst.com/scikit-learn/countvectorizer-sklearn-example/
https://dscareercommunity.springboard.com/t/k9gvm6/statistical-assumptions-for-classifiers
https://dscareercommunity.springboard.com/t/h4j0yq/machine-learning-bayesian-methods-and-text-data
https://dscareercommunity.springboard.com/t/63j13w/naive-bayes-mini-project-exercise-viii-enrichment
https://github.com/cs109/2015lab10/blob/master/TextAnalysis.ipynb
https://dscareercommunity.springboard.com/t/h4t144/naive-bayes-mini-project-help
https://github.com/rajeshdsar/Naive_bayes/blob/master/Mini_Project_Naive_Bayes.ipynb
What is log-likelihood?
- http://mathworld.wolfram.com/Likelihood.html - "Likelihood is the hypothetical probability that an event that has already occurred would yield a specific outcome. The concept differs from that of a probability in that a probability refers to the occurrence of future events, while a likelihood refers to past events with known outcomes."
- http://mathworld.wolfram.com/LikelihoodFunction.html
- http://mathworld.wolfram.com/Log-LikelihoodFunction.html
- https://www.statlect.com/glossary/log-likelihood
- https://newonlinecourses.science.psu.edu/stat504/node/27/
Why optimize max log probability instead of probability?
- https://blog.metaflow.fr/ml-notes-why-the-log-likelihood-24f7b6c40f83
- https://stats.stackexchange.com/questions/174481/why-to-optimize-max-log-probability-instead-of-probability
- https://en.wikipedia.org/wiki/Likelihood_function#Log-likelihood
Super helpful https://github.com/cs109/2015lab10/blob/master/TextAnalysis.ipynb https://github.com/rajeshdsar/Naive_bayes/blob/master/Mini_Project_Naive_Bayes.ipynb
