SUPERVISED LEARNING:
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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.
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REGRESSION\\
For continuous tgt values 

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DECISON TREES:
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  * 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,