Decision Tree

Plant

FDC

+

Data(x, y)

Plant

FDC

+

Data(x, y)

Plant

FDC

+

FDC

; y

Data(x, y)

Plant

FDC

+

FDC

; y

Data

y

,

yy = ?()

y

,

yy = ?()

y New Data

yes

No

,

y 0/1, /, /

y ?

Prediction = Classification

(y) (x) (f) , {(x,y)} y = f ( x ) f

, , ,

: y ? => 6 => => => =>

Prediction Y y ? , x ??? X predictive variable

X Y . Y , 100%

Y , .

Predictive Analytics

A new fancy name of Supervised data mining, or Regression and Classification

Find a mapping/function f such that y = f(x) given data set D = {(x,y)}

Regression when y is continuous Classification when y is categorical/binary

Predictive Analytics

Regression Multiple Linear Regression k-NN Decision Tree Regression Neural Networks

Classification Logistic Linear Regression, Discriminant Analysis k-NN, Nave Bayese Decision Tree Classifier Neural Networks SVM

Decision Tree

15

tree?

; : :

()

Trees and RulesGoal: Classify or predict an outcome based on a set of

predictorsThe output is a set of rulesExample: Goal: classify a record as will accept credit card

offer or will not accept Rule might be IF (Income > 92.5) AND (Education

< 1.5) AND (Family

: .

:

, xi xi, si, ()

= xi si

, 2

:

: 24

= ,

Income Lot_Size Ownership60.0 18.4 owner85.5 16.8 owner64.8 21.6 owner61.5 20.8 owner87.0 23.6 owner110.1 19.2 owner108.0 17.6 owner82.8 22.4 owner69.0 20.0 owner93.0 20.8 owner51.0 22.0 owner81.0 20.0 owner75.0 19.6 non-owner52.8 20.8 non-owner64.8 17.2 non-owner43.2 20.4 non-owner84.0 17.6 non-owner49.2 17.6 non-owner59.4 16.0 non-owner66.0 18.4 non-owner47.4 16.4 non-owner33.0 18.8 non-owner51.0 14.0 non-owner63.0 14.8 non-owner

,

E.g. 14.4 (14.0 14.8 )

lotsize > 14.4 lotsize < 14.4

, 15.4 (14.8 16.0 )

Note:

E.g., A, B, C 3{A} and {B, C}{B} and {A, C}{C} and {A, B}

, XLMiner

: Lot Size = 19,000

: Income = $84,000

X . ? : X = {B, B, B, B, W, W, W, W} ? Y = {B, B, W, W, W, W, W, W} ? Z = {W, W, W, W, W, W, W, W} ? Z= {B, B, B, B, B, B, B, B} ? X= {B, W} ?

?

M A

p = k A

I(A) = 0 (= 0.50, )

Note: XLMiner uses a variant called delta splitting rule

I(A) = 1 -

p = k A(m)

0() log2(m) ()

().

, .

.

.

()

(, , )

.

.E.g., If lot size < 19, and if income > 84.75, then class = owner

, .

.

=0.5.

= 0.75: 75% 1 1 .

100% .

, .

.

, .

CHAID

CART CHAID.

.

CART, .

.

.

, . .

CC(T) = Err(T) = = ()

.

.

CC(T) = Err(T) + L(T)

.

:.

/.

UP Sell

30 4,887

295,123 ?

Upselling

HOW? IF THEN rule

11 & 787 (Platinum 93.1%)

48 & 10 & 151 (Platinum 92.7%)

7 & 24 & 11 & 90 (Platinum 93.3%)

Sales bidding

- Spec ,

-

56

2008

( L )

57

,

CT

(CT)

.

RMSE ().

.

.

.

, .

2 3 4 5 6 7 8 Prediction = Classification PredictionPredictive AnalyticsPredictive Analytics Decision Tree 15 16 17 tree ? ( )Trees and Rules : 25 Note: : Lot Size = 19,000 : Income = $84,000 CHAID UP Sell 50 51 52 53Sales bidding 57 CT