R XG中的权重是否会提高线性模型中的系数/估计值？

我使用xgboost功能来优化我的模型，它可以预测二手车的价格

index <- sample((1:nrow(x)), round(0.8*nrow(x)))
training <- x[index,]
testing <- x[-index,]

model.gb <- xgboost(data = as.matrix(training[,-1]), label = as.vector(training[,1]),
                    booster = "gblinear", nround = 10)
xgb.importance(model = model.gb)

              Feature       Weight
 1:       Fossil_Fuel  3.61236e+03
 2:      newtypeother  1.01321e+03
 3: manucountryEurope  9.93548e+02
 4:   manucountryAsia  9.39839e+02
 5:    newcolorcustom  9.00756e+02
 6:      newtypesmall -8.53919e+02
 7:       newtitlenot  3.77962e+02
 8:     newcolorother  1.84748e+02
 9:               age -8.66712e+01
10:          odometer -6.80956e-03

索引
model.saturated <- lm(price~.,data = training)
model.aic.back <- MASS::stepAIC(model.saturated, direction = "backward", trace = 0)
model.aic.back %>% summary()

Call:
lm(formula = price ~ odometer + age + manucountryAsia + manucountryEurope + 
    newtypeother + newtypesmall + newcolorcustom, data = training)

Residuals:
     Min       1Q   Median       3Q      Max 
-15344.7  -2818.1     78.7   3937.1  21003.2 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        1.502e+04  2.142e+02  70.126  < 2e-16 ***
odometer          -3.312e-02  1.642e-03 -20.163  < 2e-16 ***
age               -1.400e+02  1.053e+01 -13.302  < 2e-16 ***
manucountryAsia    3.637e+02  1.636e+02   2.223  0.02627 *  
manucountryEurope  6.636e+02  2.469e+02   2.688  0.00721 ** 
newtypeother      -1.146e+03  3.467e+02  -3.307  0.00095 ***
newtypesmall      -2.111e+03  1.591e+02 -13.264  < 2e-16 ***
newcolorcustom     6.116e+02  4.166e+02   1.468  0.14208    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 5583 on 5693 degrees of freedom
Multiple R-squared:  0.1435,    Adjusted R-squared:  0.1425 
F-statistic: 136.3 on 7 and 5693 DF,  p-value: < 2.2e-16