#Câu 1
model_1 <- lm(hp ~ wt + qsec, data = mtcars)
(mtcars)## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2summary(model_1)##
## Call:
## lm(formula = hp ~ wt + qsec, data = mtcars)
##
## Residuals:
## Min 1Q Median 3Q Max
## -46.192 -17.334 -4.859 11.234 98.410
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 441.263 64.639 6.827 1.70e-07 ***
## wt 38.670 5.957 6.492 4.17e-07 ***
## qsec -23.474 3.262 -7.197 6.36e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 31.95 on 29 degrees of freedom
## Multiple R-squared: 0.7968, Adjusted R-squared: 0.7828
## F-statistic: 56.87 on 2 and 29 DF, p-value: 9.205e-11rmse_1 <- sqrt(mean(model_1$residuals^2))
cat("RMSE của mô hình hồi quy tuyến tính:", round(rmse_1, 40))## RMSE của mô hình hồi quy tuyến tính: 30.41767#Câu 2
library(nnet)
set.seed(42)
sample_index <- sample(1:nrow(iris), size = 0.7 * nrow(iris))
train_data <- iris[sample_index, ]
test_data <- iris[-sample_index, ]
#trace = FALSE: ẩn các dòng log tối ưu hóa của mô hình
model_2 <- multinom(Species ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width,
data = train_data,
trace = FALSE)
predictions <- predict(model_2, newdata = test_data)
accuracy <- sum(predictions == test_data$Species) / nrow(test_data)
cat("Accuracy trên tập Test:", round(accuracy * 100, 2), "%\n")## Accuracy trên tập Test: 97.78 %table(Dự_đoán = predictions, Thực_tế = test_data$Species)## Thực_tế
## Dự_đoán setosa versicolor virginica
## setosa 12 0 0
## versicolor 0 15 1
## virginica 0 0 17