งานนำเสนอเรื่อง: "Decision Tree Learning Dr.Yodthong Rodkaew. Decision Tree A decision Tree consists of 3 types of nodes:- 1. Decision nodes - commonly represented by squares."— ใบสำเนางานนำเสนอ:
Decision Tree Learning Dr.Yodthong Rodkaew
Decision Tree A decision Tree consists of 3 types of nodes:- 1. Decision nodes - commonly represented by squares 2. Chance nodes - represented by circles 3. End nodes - represented by triangles
Decision Tree Ex. Our friend David is the manager of a famous golf club. Sadly, he is having some trouble with his customer attendance. There are days when everyone wants to play golf and the staff are overworked. On other days, for no apparent reason, no one plays golf and staff have too much slack time. David’s objective is to optimise staff availability by trying to predict when people will play golf. To accomplish that he needs to understand the reason people decide to play and if there is any explanation for that.
Decision Tree Ex. He assumes that weather must be an important underlying factor, so he decides to use the weather forecast for the upcoming week. So during two weeks he has been recording: The outlook, whether it was sunny, overcast or raining. The temperature (in degrees Fahrenheit). The relative humidity in percent. Whether it was windy or not. Whether people attended the golf club on that day. David compiled this dataset into a table containing 14 rows and 5 columns as shown below.
Decision Tree Ex.
three different groups were found: One that plays golf when the weather is sunny, One that plays when the weather is cloudy, and One that plays when it's raining. ปกคลุมด้วยเมฆมากแดดฝนตก ความชื้น ลมแรง
Entropy สำหรับเหตุการณ์สุ่มแบบเต็มหน่วย x ซึ่งสมมุติให้มีสถานะ 1..n, เคลาด์ อี แชนนอน(Shannon) นิยามเอนโทรปี ในเทอมของ x ว่า H(x) เป็น เอนโทรปี p(i) เป็น จำนวนโอกาสของข้อมูล ที่เกิดขึ้นในการสุ่ม if the coin is fair, P(x) =1/2, H(x) =1 Full information If coin is double-headed, P(x)=1, H(x)=0 No information