SUPERVISED LEARNING:
In python, we use the method fit to train a model. Fit~train

kmeans.fit(argument)    
kmeans.predict (argument)    # Python predict() predicts the labels of the data values based on the trained model.

REGRESSION
For continuous tgt values

DECISON TREES:

• k-nearest neighbors
  • ~ “classification by proximity” ; majority vote
  • after doing it with all points, it creates a “boundary” (ie classification)
• Decision trees
  • decision by path to leaves ; measure of center
  • we ask question to narrow down areas (normally y/n Qs)
  • decision trees can surface relationships that were not evident for the human understanding.
• Random forests: decision trees tend to overfitting. A solution is 'random forest'. Is a collection of decision trees (often hundreds of them), each trained differently on the same data,