Decision Trees - another method of supervised learning

• Given a banch of attributs or variables used to decide a classification by constructing a flowchart.

• The algorithem predicts a decision based on a given attributes.Our goal is to reach concrete decisions in the early stages.
• For each step,find the attribute we can use to partition the data set to
  minimize the entropy of the data at the next step and and reach
  a definitive answer in as few steps as possible.
• decision trees are very susceptible to overfitting. Thats why we will use a technique called Random Forests.
  Random Forests is basically a "forest" of decision trees or alternate decision trees
  to Vote on the final classification.
  Random Forests randomly re-samples the input data for each tree and also randomizes
  a subset of attributes each step it allows to choose from.