prediction
What Is Prediction?
•Prediction is similar to classification
–First, construct a model
–Second, use model to predict unknown value
•Major method for prediction is regression
–Linear and multiple regression
–Non-linear regression
•Prediction is different from classification
–Classification refers to predict categorical class label
–Prediction models continuous-valued functions
Predictive Modeling in Databases
•Predictive modeling:
–Predict data values or construct generalized linear models based on the database data.
–predict value ranges or category distributions
•Method outline:
– Minimal generalization
– Attribute relevance analysis
– Generalized linear model construction
– Prediction
•Determine the major factors which influence the prediction
–Data relevance analysis: uncertainty measurement, entropy analysis, expert judgement, etc.
•Multi-level prediction: drill-down and roll-up analysis
Regress Analysis and Log-Linear Models in Prediction :
•Linear regression: Y = a + b X
–Two parameters , a and b specify the (Y-intercept and slope of the) line and are to be estimated by using the data at hand.
–using the least squares criterion to the known values of (X1,Y1), (X2,Y2), …, (Xs,Ys)
•Multiple regression: Y = a + b1 X1 + b2 X2.
–More than one predictor variable
–Many nonlinear functions can be transformed into the above.
•Nonlinear regression: Y = a + b1 X + b2 X2 + b3 X3.
•Log-linear models:
–They approximate discrete multidimensional probability distributions (multi-way table of joint probabilities) by a product of lower-order tables.
–Probability: p(a, b, c, d) = aab baccad dbcd