Basic Math Concepts – Decision Tree Regression

Concept Why It’s Needed
Mean (Average) To calculate the predicted value at each leaf node.
Difference / Subtraction To measure how far each prediction is from the actual value.
Squares and Squared Error Used in computing how “wrong” a prediction is (error = (actual – predicted)²).
Comparison Operators (<, >, ==) To decide how to split the data at each decision point.
Logic (if/else) To navigate through the tree for splitting and prediction.
Counting (Length, Frequency) To decide when to stop splitting (e.g., few samples left).
Basic Understanding of Tables / Rows / Columns To handle data structured like CSVs or lists of lists.

Decision Tree Regression – Decision Tree Regression Use Case Checklist