弹性网络回归（glmnet）预测测试数据中所有观察值的相同值

Question

我正在使用following教程在我自己的数据上尝试脊，套索和弹性网络回归。但是，我得到的所有行的预测值都不相同，因此我也得到了相同的拟合值和mse值。

我真的很感激如果有人在R中比我更了解我的代码并且可能会指出我做错了什么。这是：

library (glmnet)
require(caTools)
set.seed(111) 

new_flat <- fread('RED_SAMPLED_DATA_WITH_HEADERS.csv', header=TRUE, sep = ',') 
sample = sample.split(new_flat$SUBSCRIPTION_ID, SplitRatio = .80)
train = subset(new_flat, sample == TRUE)
test = subset(new_flat, sample == FALSE)

x=model.matrix(c201512_TOTAL_MARGIN~.-SUBSCRIPTION_ID,data=train) 
y=train$c201512_TOTAL_MARGIN

x1=model.matrix(c201512_TOTAL_MARGIN~.-SUBSCRIPTION_ID,data=test) 
y1=test$c201512_TOTAL_MARGIN



# Fit models:
fit.lasso <- glmnet(x, y, family="gaussian", alpha=1)
fit.ridge <- glmnet(x, y, family="gaussian", alpha=0)
fit.elnet <- glmnet(x, y, family="gaussian", alpha=.5)


# 10-fold Cross validation for each alpha = 0, 0.1, ... , 0.9, 1.0
fit.lasso.cv <- cv.glmnet(x, y, type.measure="mse", alpha=1, 
                          family="gaussian")
fit.ridge.cv <- cv.glmnet(x, y, type.measure="mse", alpha=0,
                          family="gaussian")
fit.elnet.cv <- cv.glmnet(x, y, type.measure="mse", alpha=.5,
                          family="gaussian")

for (i in 0:10) {
  assign(paste("fit", i, sep=""), cv.glmnet(x, y, type.measure="mse", 
                                            alpha=i/10,family="gaussian"))
}


# Plot solution paths:
par(mfrow=c(3,2))
# For plotting options, type '?plot.glmnet' in R console
plot(fit.lasso, xvar="lambda")
plot(fit10, main="LASSO")

plot(fit.ridge, xvar="lambda")
plot(fit0, main="Ridge")

plot(fit.elnet, xvar="lambda")
plot(fit5, main="Elastic Net")


yhat0 <- predict(fit0, s=fit0$lambda.1se, newx=x1)
yhat1 <- predict(fit1, s=fit1$lambda.1se, newx=x1)
yhat2 <- predict(fit2, s=fit2$lambda.1se, newx=x1)
yhat3 <- predict(fit3, s=fit3$lambda.1se, newx=x1)
yhat4 <- predict(fit4, s=fit4$lambda.1se, newx=x1)
yhat5 <- predict(fit5, s=fit5$lambda.1se, newx=x1)
yhat6 <- predict(fit6, s=fit6$lambda.1se, newx=x1)
yhat7 <- predict(fit7, s=fit7$lambda.1se, newx=x1)
yhat8 <- predict(fit8, s=fit8$lambda.1se, newx=x1)
yhat9 <- predict(fit9, s=fit9$lambda.1se, newx=x1)
yhat10 <- predict(fit10, s=fit10$lambda.1se, newx=x1)

mse0 <- mean((y1 - yhat0)^2)
mse1 <- mean((y1 - yhat1)^2)
mse2 <- mean((y1 - yhat2)^2)
mse3 <- mean((y1 - yhat3)^2)
mse4 <- mean((y1 - yhat4)^2)
mse5 <- mean((y1 - yhat5)^2)
mse6 <- mean((y1 - yhat6)^2)
mse7 <- mean((y1 - yhat7)^2)
mse8 <- mean((y1 - yhat8)^2)
mse9 <- mean((y1 - yhat9)^2)
mse10 <- mean((y1 - yhat10)^2)

编辑：代码中的图表看起来像

Answer 1

尝试在预测功能中使用s=fit0$lambda.min而不是s=fit0$lambda.1se。你的系数在套索上很快降至0，因此s=fit0$lambda.1se可能是一个过高的惩罚因素。 lambda确定系数惩罚的权重，如果它太高，你的系数将为零，并且预测将等于截距，这是因变量的平均值，例如： Y = 0.48 + 0 * X