A binary decision tree is a graph that uses a set of boolean tests {ti, t2,...tmj to classify a set of objects {xi, x2,... Vn) into correct categories. Each object has a corresponding boolean value for each test as well as a correct classification. For example, suppose our objects are mushrooms and we want to tell whether or not they are poisonous. We have the following set of mushrooms: Mushroom Spotted |Yellow Smelly Poisonous T1 Yes Yes Yes Yes T3 T4 T7 T8 Yes We have the following three tests: {isSpotted, isYellow, isSmellyj and the following two cate- gories: {Poisonous, Not Poisonous}. We can use these and our observations to construct the following tree: isSpotted Yes isSmelly isYellouw Y es es Poisonous sr ellow Poisonous Not Poisonous es Poisonous Not Poisonous A binary decision tree is a graph that uses a set of boolean tests {ti, t2,...tmj to classify a set of objects {xi, x2,... Vn) into correct categories. Each object has a corresponding boolean value for each test as well as a correct classification. For example, suppose our objects are mushrooms and we want to tell whether or not they are poisonous. We have the following set of mushrooms: Mushroom Spotted |Yellow Smelly Poisonous T1 Yes Yes Yes Yes T3 T4 T7 T8 Yes We have the following three tests: {isSpotted, isYellow, isSmellyj and the following two cate- gories: {Poisonous, Not Poisonous}. We can use these and our observations to construct the following tree: isSpotted Yes isSmelly isYellouw Y es es Poisonous sr ellow Poisonous Not Poisonous es Poisonous Not Poisonous