Start matter
Load and clean data
data <- read.csv2(here::here("data", "sakulatordata2.csv"))
# Make categorical
data$case_subtype <- factor(data$case_subtype)
data$case_court <- factor(data$case_court)
data$case_date_start <- ymd(data$case_date_start)
data$case_month_start <- as.character(data$case_date_start, format="%m")
data$case_time_court_hearing_log <- log1p(data$case_time_court_hearing)
Create training and test datasets
# Set seed
set.seed(1234)
# Shuffle data
rows <- sample(nrow(data))
data <- data[rows, ]
# Get row numbers for the training data
trainRowNumbers <- createDataPartition(data$case_time_court_hearing_log, p=0.80, list=FALSE)
# Create the training dataset
trainData <- data[trainRowNumbers,]
# Create the test dataset
testData <- data[-trainRowNumbers,]
# Store X and Y for later use.
y = trainData$case_time_court_hearing_log
y2 = testData$case_time_court_hearing_log
Train data
One-hot encoding of categorical data (if needed)
dummies_model <-
dummyVars(
case_time_court_hearing_log ~ case_priority + case_n_trans + case_subtype + case_n_part +
case_n_witnesses + case_time_lowercourt + case_court,
data = trainData
)
trainData_mat <- predict(dummies_model, newdata = trainData)
trainData <- data.frame(trainData_mat)
str(trainData)
## 'data.frame': 9646 obs. of 17 variables:
## $ case_priority : num 0 0 0 0 0 0 0 1 0 0 ...
## $ case_n_trans : num 0 0 0 0 0 4 0 1 0 0 ...
## $ case_subtype.Fagdommersak : num 0 0 0 0 1 0 0 1 0 0 ...
## $ case_subtype.Lagrettesak : num 0 0 1 1 0 0 0 0 0 0 ...
## $ case_subtype.Meddomsrettssak...begrenset.anke : num 0 0 0 0 0 0 0 0 0 0 ...
## $ case_subtype.Meddomsrettssak...bevisanke..gammel.ordning.: num 1 1 0 0 0 0 1 0 1 0 ...
## $ case_subtype.Meddomsrettssak...bevisanke.over.6.Ã¥r : num 0 0 0 0 0 0 0 0 0 0 ...
## $ case_subtype.Meddomsrettssak...bevisanke.under.6.Ã¥r : num 0 0 0 0 0 1 0 0 0 1 ...
## $ case_n_part : num 2 2 3 2 2 2 2 2 2 3 ...
## $ case_n_witnesses : num 0 0 0 0 0 0 0 0 0 0 ...
## $ case_time_lowercourt : num 99 137 152 295 69 64 59 29 110 81 ...
## $ case_court.Agder.Lagmannsrett : num 1 0 0 1 0 0 0 0 0 0 ...
## $ case_court.Borgarting.Lagmannsrett : num 0 0 0 0 1 0 0 1 0 0 ...
## $ case_court.Eidsivating.Lagmannsrett : num 0 0 0 0 0 0 0 0 0 0 ...
## $ case_court.Frostating.Lagmannsrett : num 0 0 0 0 0 1 0 0 0 1 ...
## $ case_court.Gulating.Lagmannsrett : num 0 1 0 0 0 0 0 0 1 0 ...
## $ case_court.HÃ¥logaland.Lagmannsrett : num 0 0 1 0 0 0 1 0 0 0 ...
Pre-processing (if needed)
#preProcess_range_model <- preProcess(trainData, method='range')
#trainData <- predict(preProcess_range_model, newdata = trainData)
# Append Y variable
trainData$case_time_court_hearing_log <- y
Train model
fitControl <- trainControl(
method = "repeatedcv",
number = 10,
repeats = 5)
set.seed(1234)
model = train(
case_time_court_hearing_log ~ .,
data = trainData,
method = 'ranger',
trControl = fitControl,
metric = "RMSE",
importance = 'impurity',
na.action = na.omit
)
model
## Random Forest
##
## 9646 samples
## 17 predictor
##
## No pre-processing
## Resampling: Cross-Validated (10 fold, repeated 5 times)
## Summary of sample sizes: 8658, 8658, 8656, 8658, 8656, 8656, ...
## Resampling results across tuning parameters:
##
## mtry splitrule RMSE Rsquared MAE
## 2 variance 0.4802062 0.6909144 0.3698157
## 2 extratrees 0.5168724 0.6452251 0.4063584
## 9 variance 0.4579669 0.6987067 0.3408681
## 9 extratrees 0.4530770 0.7048395 0.3366680
## 17 variance 0.4770204 0.6754078 0.3563168
## 17 extratrees 0.4710280 0.6825491 0.3506846
##
## Tuning parameter 'min.node.size' was held constant at a value of 5
## RMSE was used to select the optimal model using the smallest value.
## The final values used for the model were mtry = 9, splitrule = extratrees
## and min.node.size = 5.
Plot model
plot(model, main="Model Accuracies")
Variable importance
varimp <- varImp(model)
plot(varimp, main="Variable Importance")
Test data
One-hot dummy coding
dummies_model2 <-
dummyVars(
case_time_court_hearing_log ~ case_priority + case_n_trans + case_subtype + case_n_part +
case_n_witnesses + case_time_lowercourt + case_court,
data = testData
)
testData_mat <- predict(dummies_model2, newdata = testData)
testData <- data.frame(testData_mat)
str(testData)
## 'data.frame': 2410 obs. of 17 variables:
## $ case_priority : num 0 0 1 0 0 0 0 0 0 0 ...
## $ case_n_trans : num 2 1 0 0 1 0 0 0 0 1 ...
## $ case_subtype.Fagdommersak : num 0 0 1 0 1 0 1 0 0 0 ...
## $ case_subtype.Lagrettesak : num 0 1 0 0 0 0 0 0 0 1 ...
## $ case_subtype.Meddomsrettssak...begrenset.anke : num 0 0 0 1 0 0 0 1 0 0 ...
## $ case_subtype.Meddomsrettssak...bevisanke..gammel.ordning.: num 1 0 0 0 0 1 0 0 1 0 ...
## $ case_subtype.Meddomsrettssak...bevisanke.over.6.Ã¥r : num 0 0 0 0 0 0 0 0 0 0 ...
## $ case_subtype.Meddomsrettssak...bevisanke.under.6.Ã¥r : num 0 0 0 0 0 0 0 0 0 0 ...
## $ case_n_part : num 2 2 2 2 2 2 2 2 2 2 ...
## $ case_n_witnesses : num 0 5 1 0 0 6 0 0 0 0 ...
## $ case_time_lowercourt : num 192 43 115 92 88 71 44 70 50 70 ...
## $ case_court.Agder.Lagmannsrett : num 0 0 0 0 0 0 1 0 0 0 ...
## $ case_court.Borgarting.Lagmannsrett : num 1 1 1 0 1 0 0 0 0 0 ...
## $ case_court.Eidsivating.Lagmannsrett : num 0 0 0 0 0 1 0 0 0 0 ...
## $ case_court.Frostating.Lagmannsrett : num 0 0 0 0 0 0 0 1 0 0 ...
## $ case_court.Gulating.Lagmannsrett : num 0 0 0 1 0 0 0 0 1 1 ...
## $ case_court.HÃ¥logaland.Lagmannsrett : num 0 0 0 0 0 0 0 0 0 0 ...
Pre-processing (whenever needed)
#preProcess_range_model2 <- preProcess(testData, method='range')
#testData <- predict(preProcess_range_model2, newdata = testData)
# Append Y variable
testData$case_time_court_hearing_log <- y2
Predict on testData
predicted <- predict(model, testData)
head(predicted)
## [1] 2.379718 2.770529 1.577985 1.448289 1.173735 2.258025
Actual and predicted values
modelEval <- cbind(testData$case_time_court_hearing_log, predicted)
colnames(modelEval) <- c('Actual', 'Predicted')
modelEval <- as.data.frame(modelEval)
# inverse log transformation for predicted and actual values
modelEval$Actual <- expm1(modelEval$Actual)
modelEval$Predicted <- expm1(modelEval$Predicted)
modelEval
## Actual Predicted
## 1 32.5 9.801855
## 2 15.0 14.967079
## 3 5.5 3.845183
## 4 6.0 3.255825
## 5 2.0 2.234050
## 6 4.5 8.564178
## 7 4.5 1.976181
## 8 8.5 3.965892
## 9 7.0 5.886072
## 10 9.0 13.657918
## 11 2.0 2.080594
## 12 3.0 2.479168
## 13 9.5 6.301180
## 14 3.0 3.797674
## 15 1.5 2.620903
## 16 6.5 9.919042
## 17 6.0 4.906865
## 18 3.0 2.312593
## 19 56.5 31.470215
## 20 16.0 21.680819
## 21 0.5 1.743132
## 22 3.0 3.918610
## 23 6.0 3.052504
## 24 32.5 24.785988
## 25 5.0 9.140646
## 26 5.5 12.587296
## 27 5.5 5.675292
## 28 1.5 1.957907
## 29 11.5 13.269082
## 30 17.5 13.176682
## 31 1.5 2.910751
## 32 2.0 2.334560
## 33 2.0 2.416585
## 34 31.0 5.697695
## 35 3.0 11.119581
## 36 1.0 2.078343
## 37 1.5 2.047773
## 38 7.5 9.550895
## 39 31.5 7.570438
## 40 2.5 2.038603
## 41 6.5 12.218163
## 42 43.5 15.406495
## 43 35.0 28.571031
## 44 12.5 14.947420
## 45 16.5 22.745800
## 46 23.5 10.241052
## 47 4.0 13.946450
## 48 13.5 16.783660
## 49 12.5 13.735778
## 50 10.5 10.950728
## 51 2.0 2.149927
## 52 1.5 4.377719
## 53 30.5 12.202874
## 54 5.5 3.089051
## 55 1.5 1.761577
## 56 12.0 17.330758
## 57 10.0 11.722836
## 58 5.5 5.311247
## 59 3.5 4.344745
## 60 9.0 5.780397
## 61 3.0 2.135857
## 62 7.0 3.321013
## 63 17.5 22.149833
## 64 19.5 13.302578
## 65 2.5 2.927933
## 66 2.5 6.049258
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## 74 6.0 2.917686
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## 80 36.0 16.674944
## 81 2.5 8.402838
## 82 21.0 20.296096
## 83 5.0 3.037055
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## 88 2.5 2.338641
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## 1805 11.5 1.690483
## 1806 16.0 16.796570
## 1807 7.0 8.520886
## 1808 2.5 30.952554
## 1809 21.0 19.354050
## 1810 2.5 9.125539
## 1811 18.0 23.770831
## 1812 16.0 2.910658
## 1813 10.0 31.795617
## 1814 19.5 2.214165
## 1815 5.5 11.931536
## 1816 16.5 2.447363
## 1817 1.5 9.564830
## 1818 13.5 6.977573
## 1819 2.0 6.499311
## 1820 12.0 3.123405
## 1821 5.5 2.036928
## 1822 7.0 2.938649
## 1823 3.0 8.679219
## 1824 2.0 28.243072
## 1825 3.0 2.087579
## 1826 4.5 13.708595
## 1827 49.0 3.120622
## 1828 1.0 5.431947
## 1829 6.0 2.301548
## 1830 3.0 18.804738
## 1831 2.5 2.228722
## 1832 2.0 6.330134
## 1833 14.5 6.320523
## 1834 1.0 5.367103
## 1835 16.5 12.347426
## 1836 11.0 25.236192
## 1837 5.5 2.488939
## 1838 33.0 13.327519
## 1839 25.0 7.070772
## 1840 2.5 10.692914
## 1841 6.0 33.195370
## 1842 27.0 17.121304
## 1843 10.5 2.167558
## 1844 39.5 3.439098
## 1845 40.5 11.817197
## 1846 2.0 15.228232
## 1847 2.5 2.598222
## 1848 15.0 11.693691
## 1849 15.0 5.939499
## 1850 3.0 24.782976
## 1851 15.5 9.938718
## 1852 5.0 5.730093
## 1853 27.0 14.174044
## 1854 41.0 13.746063
## 1855 3.0 2.358005
## 1856 15.0 2.327575
## 1857 0.5 16.140192
## 1858 5.5 2.063612
## 1859 2.5 14.544865
## 1860 16.0 3.774766
## 1861 2.5 11.286341
## 1862 14.0 3.928414
## 1863 2.0 3.043457
## 1864 34.5 21.325790
## 1865 3.0 9.587956
## 1866 4.5 6.822150
## 1867 23.0 1.902191
## 1868 4.5 1.682458
## 1869 7.5 3.652983
## 1870 2.0 1.722760
## 1871 1.0 2.301533
## 1872 4.0 3.459032
## 1873 2.5 3.452481
## 1874 1.5 12.797622
## 1875 1.5 7.662223
## 1876 2.5 10.534856
## 1877 11.5 5.465974
## 1878 5.0 2.861993
## 1879 14.0 17.337528
## 1880 7.5 5.692868
## 1881 2.5 2.782293
## 1882 41.0 11.592491
## 1883 6.5 2.287246
## 1884 3.5 8.455228
## 1885 6.0 6.147373
## 1886 1.0 4.917301
## 1887 8.0 3.759904
## 1888 7.0 2.588875
## 1889 5.0 11.165410
## 1890 22.0 2.914044
## 1891 2.5 2.833095
## 1892 5.0 14.961023
## 1893 3.0 4.123365
## 1894 2.5 4.021591
## 1895 11.0 5.854671
## 1896 12.0 5.292329
## 1897 2.5 21.002628
## 1898 4.5 19.112110
## 1899 5.5 8.826440
## 1900 23.0 2.015918
## 1901 13.0 14.755018
## 1902 12.5 2.341584
## 1903 3.0 14.640940
## 1904 10.5 10.707811
## 1905 1.0 7.084936
## 1906 15.0 3.180848
## 1907 44.0 17.576617
## 1908 5.5 6.601413
## 1909 5.0 9.361771
## 1910 17.0 17.690890
## 1911 4.5 7.557755
## 1912 6.5 6.089483
## 1913 47.0 2.894466
## 1914 7.5 30.720061
## 1915 8.5 25.784442
## 1916 3.0 1.715460
## 1917 34.0 2.091647
## 1918 23.5 4.436109
## 1919 3.0 2.521037
## 1920 3.0 4.849652
## 1921 5.0 14.791298
## 1922 1.5 12.636958
## 1923 2.5 15.780860
## 1924 16.5 2.207632
## 1925 11.0 14.756642
## 1926 15.5 13.488724
## 1927 1.0 1.881321
## 1928 12.5 3.303843
## 1929 11.0 2.706873
## 1930 2.0 10.301776
## 1931 2.5 7.437984
## 1932 2.0 22.754463
## 1933 13.0 12.535253
## 1934 2.5 6.090476
## 1935 50.5 3.346135
## 1936 45.0 3.135258
## 1937 28.0 4.633563
## 1938 2.0 2.917849
## 1939 2.0 5.562400
## 1940 7.0 15.365699
## 1941 3.5 15.323509
## 1942 4.5 2.812642
## 1943 9.5 5.794221
## 1944 20.5 2.453315
## 1945 4.0 23.179973
## 1946 1.5 15.258226
## 1947 2.5 26.670429
## 1948 47.0 2.208598
## 1949 10.0 6.599128
## 1950 43.5 2.814378
## 1951 3.5 18.391568
## 1952 3.5 21.287385
## 1953 1.5 14.933671
## 1954 24.5 5.487929
## 1955 14.5 2.055242
## 1956 18.0 10.667225
## 1957 2.5 2.277271
## 1958 1.5 16.144883
## 1959 10.0 16.371168
## 1960 2.0 16.777299
## 1961 16.5 16.371674
## 1962 9.5 9.718422
## 1963 6.0 6.359482
## 1964 10.5 3.176731
## 1965 16.5 5.503521
## 1966 8.0 28.108184
## 1967 3.5 2.455905
## 1968 6.0 8.833262
## 1969 11.0 3.715739
## 1970 2.5 2.456606
## 1971 16.5 29.627081
## 1972 1.5 4.176347
## 1973 2.5 5.770451
## 1974 14.0 1.784631
## 1975 4.5 18.455535
## 1976 4.5 15.580677
## 1977 2.5 12.546996
## 1978 18.0 9.403919
## 1979 18.5 5.270371
## 1980 16.0 12.310444
## 1981 12.5 2.257740
## 1982 5.0 2.018404
## 1983 15.5 3.607872
## 1984 1.5 4.796774
## 1985 1.5 1.836035
## 1986 4.0 2.442211
## 1987 8.0 2.419619
## 1988 2.0 16.855046
## 1989 1.5 4.750676
## 1990 2.5 9.103145
## 1991 9.0 5.938686
## 1992 5.5 36.618917
## 1993 8.0 2.710938
## 1994 5.5 12.890886
## 1995 26.0 13.682473
## 1996 7.5 6.330017
## 1997 19.0 18.696259
## 1998 10.0 19.635985
## 1999 10.5 19.875968
## 2000 23.5 2.066352
## 2001 27.5 15.737107
## 2002 15.5 2.351747
## 2003 2.0 21.672656
## 2004 13.5 6.156068
## 2005 1.5 2.029305
## 2006 12.0 13.674533
## 2007 6.5 5.594930
## 2008 2.0 9.300295
## 2009 8.5 8.154755
## 2010 23.0 16.439281
## 2011 7.0 12.811943
## 2012 13.0 13.569843
## 2013 17.0 10.416074
## 2014 6.0 1.738504
## 2015 9.5 2.118218
## 2016 6.0 12.458489
## 2017 1.0 26.557875
## 2018 2.0 1.740809
## 2019 20.5 5.160099
## 2020 22.5 3.625919
## 2021 1.5 16.739518
## 2022 11.0 12.281064
## 2023 3.0 2.060864
## 2024 2.0 9.601799
## 2025 9.5 16.051618
## 2026 12.0 12.781393
## 2027 2.0 10.250099
## 2028 7.0 5.794221
## 2029 20.5 20.304041
## 2030 15.0 2.085525
## 2031 6.0 14.824874
## 2032 12.0 21.310600
## 2033 28.0 2.474588
## 2034 2.0 10.726742
## 2035 7.0 4.058731
## 2036 10.0 6.301205
## 2037 1.5 2.119297
## 2038 11.0 17.843801
## 2039 3.5 11.266308
## 2040 4.0 3.350261
## 2041 1.5 13.873831
## 2042 22.0 12.000856
## 2043 11.0 8.042913
## 2044 5.0 6.334618
## 2045 17.5 14.308354
## 2046 12.5 3.082065
## 2047 20.0 4.515901
## 2048 16.5 2.004961
## 2049 17.5 5.159479
## 2050 1.5 2.327575
## 2051 5.5 4.342588
## 2052 2.5 2.931406
## 2053 2.0 6.046074
## 2054 2.5 5.081245
## 2055 5.5 6.806422
## 2056 4.5 25.611265
## 2057 7.5 16.025528
## 2058 5.0 15.416538
## 2059 9.0 7.240996
## 2060 26.5 2.835357
## 2061 10.5 5.238062
## 2062 11.5 3.630034
## 2063 5.0 9.316299
## 2064 2.0 6.766806
## 2065 3.0 14.264248
## 2066 3.0 3.120622
## 2067 3.0 1.534870
## 2068 6.5 2.066297
## 2069 15.0 5.968467
## 2070 1.5 2.833330
## 2071 3.0 12.797047
## 2072 4.5 4.231635
## 2073 2.5 20.258978
## 2074 2.5 9.414610
## 2075 2.0 12.490143
## 2076 4.0 16.094585
## 2077 31.5 4.315550
## 2078 18.5 14.622314
## 2079 32.5 4.564378
## 2080 14.5 8.829114
## 2081 28.5 13.648374
## 2082 8.5 5.074905
## 2083 4.5 2.318504
## 2084 12.0 16.080487
## 2085 26.0 3.995004
## 2086 12.0 4.253236
## 2087 1.0 7.839979
## 2088 11.5 4.366383
## 2089 2.5 2.593578
## 2090 9.5 7.737375
## 2091 6.0 2.098271
## 2092 6.0 4.717337
## 2093 1.5 2.077510
## 2094 7.0 10.118579
## 2095 2.5 3.371063
## 2096 9.5 7.830021
## 2097 5.5 1.984219
## 2098 11.0 3.419382
## 2099 5.5 5.688729
## 2100 5.5 1.715460
## 2101 1.0 2.073077
## 2102 3.0 17.813580
## 2103 5.5 29.681382
## 2104 1.5 10.891393
## 2105 1.5 5.189040
## 2106 13.0 15.199384
## 2107 59.0 4.451159
## 2108 7.5 2.671286
## 2109 5.5 4.952671
## 2110 24.0 10.029560
## 2111 3.5 4.844089
## 2112 3.0 36.369381
## 2113 4.5 14.040261
## 2114 8.0 2.670464
## 2115 3.0 6.258896
## 2116 21.5 13.368616
## 2117 13.5 6.979357
## 2118 1.0 30.272791
## 2119 7.0 21.058698
## 2120 33.5 2.036928
## 2121 7.0 2.269582
## 2122 18.0 2.000710
## 2123 33.0 1.692775
## 2124 2.0 5.393749
## 2125 2.0 6.625899
## 2126 1.5 9.768594
## 2127 2.5 2.156651
## 2128 13.5 5.272276
## 2129 2.0 21.324816
## 2130 6.0 15.599980
## 2131 2.5 2.094223
## 2132 6.0 12.886571
## 2133 15.5 2.106231
## 2134 23.0 3.700654
## 2135 2.0 12.076147
## 2136 10.0 7.888298
## 2137 1.5 25.515002
## 2138 2.0 5.286031
## 2139 9.0 2.631074
## 2140 5.5 2.094219
## 2141 26.0 12.248726
## 2142 24.5 11.845435
## 2143 2.0 5.577652
## 2144 1.5 3.135258
## 2145 11.0 2.003358
## 2146 12.5 10.626436
## 2147 4.0 16.473721
## 2148 7.0 3.584217
## 2149 1.5 10.577063
## 2150 6.0 14.757720
## 2151 24.0 2.079772
## 2152 4.0 6.498299
## 2153 6.5 3.278566
## 2154 38.5 20.763767
## 2155 4.0 7.335450
## 2156 5.5 7.908286
## 2157 7.0 4.450819
## 2158 15.0 2.590692
## 2159 5.5 5.868502
## 2160 5.0 8.108311
## 2161 5.0 5.495520
## 2162 1.5 2.352291
## 2163 7.0 2.829197
## 2164 5.5 12.798559
## 2165 1.5 7.158076
## 2166 1.0 17.442482
## 2167 4.5 14.790711
## 2168 22.5 3.960975
## 2169 3.5 6.381509
## 2170 15.5 18.419123
## 2171 9.5 2.455744
## 2172 5.0 3.366677
## 2173 8.0 5.728829
## 2174 11.0 4.873532
## 2175 2.5 1.995009
## 2176 14.5 31.297060
## 2177 3.5 16.700177
## 2178 6.0 1.981350
## 2179 3.0 1.991695
## 2180 35.0 14.314326
## 2181 27.0 17.161697
## 2182 2.0 4.886608
## 2183 1.5 22.572533
## 2184 8.5 3.976829
## 2185 23.0 12.953541
## 2186 6.5 3.140759
## 2187 14.0 13.064970
## 2188 6.0 6.363610
## 2189 44.0 11.893886
## 2190 2.5 18.214220
## 2191 11.0 17.178768
## 2192 7.0 2.696532
## 2193 6.0 1.962816
## 2194 22.0 7.883574
## 2195 37.5 1.702396
## 2196 2.5 11.579972
## 2197 1.5 13.802979
## 2198 57.5 13.150030
## 2199 3.0 2.162209
## 2200 23.5 3.538508
## 2201 27.5 2.807326
## 2202 7.0 8.128733
## 2203 1.0 18.213201
## 2204 1.0 2.320246
## 2205 1.5 4.820557
## 2206 9.5 19.296650
## 2207 16.5 5.889701
## 2208 6.0 2.746694
## 2209 4.5 10.675794
## 2210 18.0 3.252116
## 2211 5.5 11.463748
## 2212 2.5 3.440783
## 2213 13.5 2.334077
## 2214 3.0 3.069761
## 2215 13.5 6.761791
## 2216 4.0 2.036882
## 2217 1.5 4.292009
## 2218 1.5 2.986284
## 2219 8.0 2.467307
## 2220 1.5 5.937237
## 2221 6.0 11.225176
## 2222 1.0 13.703239
## 2223 2.5 3.024645
## 2224 3.5 2.192189
## 2225 8.0 2.101654
## 2226 12.0 19.766298
## 2227 15.0 2.594191
## 2228 4.5 18.062979
## 2229 1.5 14.221720
## 2230 24.5 10.746515
## 2231 2.0 3.025790
## 2232 25.5 16.859588
## 2233 16.0 2.161392
## 2234 12.5 2.891433
## 2235 1.0 8.563771
## 2236 14.5 2.181462
## 2237 1.5 1.971978
## 2238 3.0 12.490150
## 2239 12.5 17.271910
## 2240 3.0 26.541162
## 2241 2.0 2.747387
## 2242 17.5 7.830438
## 2243 14.5 9.365382
## 2244 25.5 18.315745
## 2245 3.0 1.784631
## 2246 10.5 7.790457
## 2247 14.5 17.445654
## 2248 21.0 23.354709
## 2249 1.0 12.498300
## 2250 5.0 9.565033
## 2251 18.5 8.311991
## 2252 34.5 7.711959
## 2253 7.5 14.248198
## 2254 9.5 2.138097
## 2255 7.0 1.775032
## 2256 13.5 4.469910
## 2257 18.0 13.749800
## 2258 2.5 1.854207
## 2259 1.0 7.420547
## 2260 5.5 7.385675
## 2261 2.5 12.009968
## 2262 2.5 5.926374
## 2263 3.0 6.140213
## 2264 10.5 5.196612
## 2265 9.0 6.165837
## 2266 7.5 17.912303
## 2267 7.0 5.474496
## 2268 5.5 2.593346
## 2269 4.5 12.718311
## 2270 31.5 3.822520
## 2271 8.5 5.758480
## 2272 1.0 5.322854
## 2273 16.5 2.056759
## 2274 3.0 4.017727
## 2275 3.5 25.866283
## 2276 3.5 2.458901
## 2277 3.5 7.437519
## 2278 5.0 1.836035
## 2279 28.0 5.768371
## 2280 2.5 11.606106
## 2281 5.5 2.479168
## 2282 4.5 12.463481
## 2283 9.0 7.624946
## 2284 9.5 12.898685
## 2285 2.0 3.535302
## 2286 2.0 4.715806
## 2287 6.0 6.979939
## 2288 9.5 2.109883
## 2289 2.0 9.346036
## 2290 3.5 15.693400
## 2291 6.0 4.810491
## 2292 3.0 13.777660
## 2293 7.0 30.557521
## 2294 17.5 13.489521
## 2295 5.0 1.907454
## 2296 20.5 2.073694
## 2297 33.0 13.666858
## 2298 12.0 2.090964
## 2299 2.0 5.968648
## 2300 2.0 17.249005
## 2301 10.5 2.060864
## 2302 2.5 12.359879
## 2303 11.0 13.469426
## 2304 12.0 7.351954
## 2305 2.0 2.193417
## 2306 12.5 13.658820
## 2307 8.5 4.687687
## 2308 12.5 1.996195
## 2309 1.0 6.753095
## 2310 10.5 12.481680
## 2311 2.5 2.415487
## 2312 1.5 1.708108
## 2313 6.0 21.008821
## 2314 5.5 14.364033
## 2315 2.0 38.936766
## 2316 2.5 1.854207
## 2317 27.0 7.326398
## 2318 7.5 25.103479
## 2319 50.5 1.708108
## 2320 2.5 13.590575
## 2321 10.0 4.454884
## 2322 33.5 3.141534
## 2323 2.0 2.768131
## 2324 12.5 22.752893
## 2325 3.0 3.999008
## 2326 1.0 10.842402
## 2327 2.5 7.481580
## 2328 19.0 2.129907
## 2329 1.5 16.862995
## 2330 6.5 2.960691
## 2331 7.5 2.006917
## 2332 4.0 12.843147
## 2333 20.0 2.171212
## 2334 6.5 2.048186
## 2335 2.0 13.061116
## 2336 7.0 15.788135
## 2337 0.5 2.109883
## 2338 6.0 14.038834
## 2339 15.5 2.056877
## 2340 7.0 4.213463
## 2341 1.5 8.888590
## 2342 11.5 5.886072
## 2343 1.0 2.016493
## 2344 3.5 18.235429
## 2345 7.5 2.073077
## 2346 5.5 12.518248
## 2347 3.5 5.311247
## 2348 33.0 2.856594
## 2349 2.0 3.931540
## 2350 15.0 13.247244
## 2351 4.5 10.521232
## 2352 1.5 14.448298
## 2353 3.0 3.204467
## 2354 28.0 5.623380
## 2355 4.0 2.934555
## 2356 6.5 6.234063
## 2357 3.0 18.938501
## 2358 4.0 10.123381
## 2359 2.5 2.467560
## 2360 13.5 3.772136
## 2361 10.0 3.393691
## 2362 17.0 18.187365
## 2363 2.0 2.267813
## 2364 6.0 2.894627
## 2365 4.5 2.875784
## 2366 24.5 17.304180
## 2367 2.0 7.314060
## 2368 2.0 12.271914
## 2369 2.5 5.585438
## 2370 16.0 10.857376
## 2371 7.0 2.188101
## 2372 13.5 14.004698
## 2373 4.5 15.224282
## 2374 33.0 2.068362
## 2375 1.5 17.352873
## 2376 21.5 9.393020
## 2377 7.5 11.551410
## 2378 1.5 2.673043
## 2379 27.5 2.419725
## 2380 9.0 7.855634
## 2381 13.0 18.271335
## 2382 2.0 18.460922
## 2383 3.5 13.311900
## 2384 6.5 2.475671
## 2385 20.5 3.166390
## 2386 12.5 2.068305
## 2387 9.5 2.470336
## 2388 5.5 13.083399
## 2389 3.5 2.451836
## 2390 1.5 6.507920
## 2391 2.0 4.607328
## 2392 44.5 2.063615
## 2393 2.0 7.946302
## 2394 8.0 10.847948
## 2395 4.5 16.247326
## 2396 1.5 1.904601
## 2397 7.0 2.067865
## 2398 9.5 6.037172
## 2399 19.0 30.806543
## 2400 1.5 3.645831
## 2401 1.5 3.127318
## 2402 16.5 7.255031
## 2403 21.5 21.055071
## 2404 5.0 8.586621
## 2405 3.5 30.038558
## 2406 10.5 11.755471
## 2407 19.0 9.801855
## 2408 21.0 14.967079
## 2409 18.5 3.845183
## 2410 13.0 3.255825
A plot: Actual versus predicted
ggplot(modelEval, aes(x = Actual, y = Predicted)) +
geom_point(alpha = 0.6, color = "cadetblue") +
geom_smooth(method = "loess", formula = "y ~ x") +
geom_abline(intercept = 0, slope = 1, linetype = 2) +
labs(title = "Predicted vs Actual")
Model evaluation
evaluation <- postResample(pred = modelEval$Predicted, obs = modelEval$Actual)
knitr::kable(evaluation, digits = 2) # Still a relatively large RMSE after predicting on the test data
| RMSE |
12.02 |
| Rsquared |
0.01 |
| MAE |
8.36 |