In machine learning, these statements are called forks, and they split the data into two branches based on some value. That value between the branches is called a split point. Homes to the left of that point get categorized in one way, while those to the right are categorized in another. A split point is the decision tree's version of a boundary. Picking a split point has tradeoffs. Our initial split ( 73 m) incorrectly classifies some San Francisco homes as New York ones.

In machine learning, these statements are called forks, and they split the data into two branches based on some value. That value between the branches is called a split point. Homes to the left of that point get categorized in one way, while those to the right are categorized in another. A split point is the decision tree's version of a boundary. Picking a split point has tradeoffs. Our initial split ( 240 ft) incorrectly classifies some San Francisco homes as New York ones.

In machine learning, these statements are called forks, and they split the data into two branches based on some value. That value between the branches is called a split point. Homes to the left of that point get categorized in one way, while those to the right are categorized in another. A split point is the decision tree's version of a boundary. Picking a split point has tradeoffs. Our initial split ( 240 ft) incorrectly classifies some San Francisco homes as New York ones.