Persamaan Aljabar Linear

library(ISLR)

data(Auto)

model <- lm(mpg ~ cylinders + displacement + horsepower + weight, data = Auto)

summary(model)

## 
## Call:
## lm(formula = mpg ~ cylinders + displacement + horsepower + weight, 
##     data = Auto)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -11.5248  -2.7964  -0.3568   2.2577  16.3221 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  45.7567705  1.5200437  30.102  < 2e-16 ***
## cylinders    -0.3932854  0.4095522  -0.960 0.337513    
## displacement  0.0001389  0.0090099   0.015 0.987709    
## horsepower   -0.0428125  0.0128699  -3.327 0.000963 ***
## weight       -0.0052772  0.0007166  -7.364 1.08e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.242 on 387 degrees of freedom
## Multiple R-squared:  0.7077, Adjusted R-squared:  0.7046 
## F-statistic: 234.2 on 4 and 387 DF,  p-value: < 2.2e-16

new_observation <- data.frame(cylinders = 8, displacement = 318, horsepower = 150, weight = 3500)

predicted_mpg <- predict(model, newdata = new_observation)

print(predicted_mpg)

##        1 
## 17.76268

library(caret)

## Loading required package: ggplot2

## Loading required package: lattice

ctrl <- trainControl(method = "cv", number = 10, verboseIter = TRUE)

metric <- "RMSE"

set.seed(123)

model_fit <- train(mpg ~ cylinders + displacement + horsepower + weight, data = Auto, method = "lm", trControl = ctrl, metric = metric)

## + Fold01: intercept=TRUE 
## - Fold01: intercept=TRUE 
## + Fold02: intercept=TRUE 
## - Fold02: intercept=TRUE 
## + Fold03: intercept=TRUE 
## - Fold03: intercept=TRUE 
## + Fold04: intercept=TRUE 
## - Fold04: intercept=TRUE 
## + Fold05: intercept=TRUE 
## - Fold05: intercept=TRUE 
## + Fold06: intercept=TRUE 
## - Fold06: intercept=TRUE 
## + Fold07: intercept=TRUE 
## - Fold07: intercept=TRUE 
## + Fold08: intercept=TRUE 
## - Fold08: intercept=TRUE 
## + Fold09: intercept=TRUE 
## - Fold09: intercept=TRUE 
## + Fold10: intercept=TRUE 
## - Fold10: intercept=TRUE 
## Aggregating results
## Fitting final model on full training set

print(model_fit)

## Linear Regression 
## 
## 392 samples
##   4 predictor
## 
## No pre-processing
## Resampling: Cross-Validated (10 fold) 
## Summary of sample sizes: 353, 352, 353, 353, 353, 354, ... 
## Resampling results:
## 
##   RMSE     Rsquared   MAE     
##   4.19486  0.7156793  3.241479
## 
## Tuning parameter 'intercept' was held constant at a value of TRUE