ML-Course-Notes
Notes for ML course at UCSF Library
Visuals
Decision Tree
Random Forest
Logistic Regression
Support Vector Machine
Neural Net
Naive Bayes
Exercises
Swap out the Random Forest for a different ML algorithm. At their defaults, how do they perform?
See if you can get better performance from the Random Forest by changing some of the parameters. What does n_estimators do?
Take a look at the parameters for a vectorizer. What does ngram_range do? Can you view the output of the ngram range?
Try programming a rules based (as opposed to machine learning) approach. Can you beat these algorithms?