Start with your overarching objective/ “big decision” at the top (root) … Draw your arrows. … Attach leaf nodes at the end of your branches. … Determine the odds of success of each decision point. … Evaluate risk vs reward.
How is a decision tree made?
At each node a variable is evaluated to decide which path to follow. When they are being built decision trees are constructed by recursively evaluating different features and using at each node the feature that best splits the data. … In a normal decision tree it evaluates the variable that best splits the data.
How do you create a decision tree in machine learning?
- Get list of rows (dataset) which are taken into consideration for making decision tree (recursively at each nodes).
- Calculate uncertanity of our dataset or Gini impurity or how much our data is mixed up etc.
- Generate list of all question which needs to be asked at that node.
