prediction

Accuracy Score ValueError: Can't Handle mix of binary and continuous target

由 左心房为你撑大大i 提交于 2019-11-26 08:59:47
问题 I\'m using linear_model.LinearRegression from scikit-learn as a predictive model. It works and it\'s perfect. I have a problem to evaluate the predicted results using the accuracy_score metric. This is my true Data : array([1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0]) My predicted Data: array([ 0.07094605, 0.1994941 , 0.19270157, 0.13379635, 0.04654469, 0.09212494, 0.19952108, 0.12884365, 0.15685076, -0.01274453, 0.32167554, 0.32167554, -0.10023553, 0.09819648, -0.06755516, 0.25390082,

How does predict.lm() compute confidence interval and prediction interval?

由 主宰稳场 提交于 2019-11-26 02:21:52
问题 I ran a regression: CopierDataRegression <- lm(V1~V2, data=CopierData1) and my task was to obtain a 90% confidence interval for the mean response given V2=6 and 90% prediction interval when V2=6 . I used the following code: X6 <- data.frame(V2=6) predict(CopierDataRegression, X6, se.fit=TRUE, interval=\"confidence\", level=0.90) predict(CopierDataRegression, X6, se.fit=TRUE, interval=\"prediction\", level=0.90) and I got (87.3, 91.9) and (74.5, 104.8) which seems to be correct since the PI

Getting Warning: “ &#39;newdata&#39; had 1 row but variables found have 32 rows” on predict.lm

由 本小妞迷上赌 提交于 2019-11-26 00:48:40
问题 I found peculiarity while using predict and lm function in R. I got different results for data frame and vector for same data. DataFrame code: data(mtcars) fitCar<-lm(mtcars$mpg~mtcars$wt) predict(fitCar, data.frame(x=mean(mtcars$wt)), interval=\"confidence\") Output: fit lwr upr 1 23.282611 21.988668 24.57655 2 21.919770 20.752751 23.08679 3 24.885952 23.383008 26.38890 4 20.102650 19.003004 21.20230 5 18.900144 17.771469 20.02882 6 18.793255 17.659216 19.92729 7 18.205363 17.034274 19.37645

Getting Warning: “ &#39;newdata&#39; had 1 row but variables found have 32 rows” on predict.lm

由 泄露秘密 提交于 2019-11-25 22:41:28
I found peculiarity while using predict and lm function in R. I got different results for data frame and vector for same data. DataFrame code: data(mtcars) fitCar<-lm(mtcars$mpg~mtcars$wt) predict(fitCar, data.frame(x=mean(mtcars$wt)), interval="confidence") Output: fit lwr upr 1 23.282611 21.988668 24.57655 2 21.919770 20.752751 23.08679 3 24.885952 23.383008 26.38890 4 20.102650 19.003004 21.20230 5 18.900144 17.771469 20.02882 6 18.793255 17.659216 19.92729 7 18.205363 17.034274 19.37645 8 20.236262 19.136179 21.33635 9 20.450041 19.347720 21.55236 10 18.900144 17.771469 20.02882 11 18
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