Logistic Regression
## Call:
## lda(Direction ~ Lag1 + Lag2, data = Smarket, subset = train)
##
## Prior probabilities of groups:
## Down Up
## 0.491984 0.508016
##
## Group means:
## Lag1 Lag2
## Down 0.04279022 0.03389409
## Up -0.03954635 -0.03132544
##
## Coefficients of linear discriminants:
## LD1
## Lag1 -0.6420190
## Lag2 -0.5135293
## [1] "class" "posterior" "x"
## Direction.2005
## lda.class Down Up
## Down 35 35
## Up 76 106
## [1] 0.5595238
## [1] 70
## [1] 182
## 999 1000 1001 1002 1003 1004 1005 1006
## 0.4901792 0.4792185 0.4668185 0.4740011 0.4927877 0.4938562 0.4951016 0.4872861
## 1007 1008 1009 1010 1011 1012 1013 1014
## 0.4907013 0.4844026 0.4906963 0.5119988 0.4895152 0.4706761 0.4744593 0.4799583
## 1015 1016 1017 1018
## 0.4935775 0.5030894 0.4978806 0.4886331
## [1] Up Up Up Up Up Up Up Up Up Up Up Down Up Up Up
## [16] Up Up Down Up Up
## Levels: Down Up
## [1] 0
## Call:
## qda(Direction ~ Lag1 + Lag2, data = Smarket, subset = train)
##
## Prior probabilities of groups:
## Down Up
## 0.491984 0.508016
##
## Group means:
## Lag1 Lag2
## Down 0.04279022 0.03389409
## Up -0.03954635 -0.03132544
## Direction.2005
## qda.class Down Up
## Down 30 20
## Up 81 121
## [1] 0.5992063
##
## Naive Bayes Classifier for Discrete Predictors
##
## Call:
## naiveBayes.default(x = X, y = Y, laplace = laplace)
##
## A-priori probabilities:
## Y
## Down Up
## 0.491984 0.508016
##
## Conditional probabilities:
## Lag1
## Y [,1] [,2]
## Down 0.04279022 1.227446
## Up -0.03954635 1.231668
##
## Lag2
## Y [,1] [,2]
## Down 0.03389409 1.239191
## Up -0.03132544 1.220765
## [1] 0.04279022
## [1] 1.227446
## Direction.2005
## nb.class Down Up
## Down 28 20
## Up 83 121
## [1] 0.5912698
## Down Up
## [1,] 0.4873164 0.5126836
## [2,] 0.4762492 0.5237508
## [3,] 0.4653377 0.5346623
## [4,] 0.4748652 0.5251348
## [5,] 0.4901890 0.5098110
## Direction.2005
## knn.pred Down Up
## Down 43 58
## Up 68 83
## [1] 0.5
## Direction.2005
## knn.pred Down Up
## Down 48 54
## Up 63 87
## [1] 0.5357143
## [1] 5822 86
## No Yes
## 5474 348
## [1] 0.05977327
## [1] 165.0378
## [1] 0.1647078
## [1] 1
## [1] 1
## [1] 0.118
## [1] 0.059
## test.Y
## knn.pred No Yes
## No 873 50
## Yes 68 9
## [1] 0.1168831
## test.Y
## knn.pred No Yes
## No 920 54
## Yes 21 5
## [1] 0.1923077
## test.Y
## knn.pred No Yes
## No 930 55
## Yes 11 4
## [1] 0.2666667
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## test.Y
## glm.pred No Yes
## No 934 59
## Yes 7 0
## test.Y
## glm.pred No Yes
## No 919 48
## Yes 22 11
## [1] 0.3333333