Analysis on the insurance claims
Archit 7 Jan 2018
Claims experience from a large midwestern (US) property and casualty insurer for private passenger automobile insurance. The dependent variable is the amount paid on a closed claim, in (US) dollars (claims that were not closed by year end are handled separately). Insurers categorize policyholders according to a risk classification system. This insurer's risk classification system is based on automobile operator characteristics and ve- hicle characteristics, and these factors are summarized by the risk class categorical variable CLASS.
- STATE - Codes 01 to 17 used, with each code randomly assigned to an actual individual state
- CLASS - Rating class of operator, based on age, gender, marital status, use of vehicle, as coded in a separate PDF file
- GENDER
- AGE
- PAID - Amount paid to settle and close a claim
Source: http://instruction.bus.wisc.edu/jfrees/jfreesbooks/Regression%20Modeling/BookWebDec2010/data.html
auto.claims = read.csv("C:/Users/Administrator/Downloads/AutoClaims.csv")
library(dplyr)
library(ggplot2)
library(corrplot)
library(reshape2)
#install.packages("xgboost")
library(xgboost)
library(caTools)head(auto.claims)## STATE CLASS GENDER AGE PAID
## 1 STATE 14 C6 M 97 1134.44
## 2 STATE 15 C6 M 96 3761.24
## 3 STATE 15 C11 M 95 7842.31
## 4 STATE 15 F6 F 95 2384.67
## 5 STATE 15 F6 M 95 650.00
## 6 STATE 15 F6 M 95 391.12
ggplot(auto.claims,aes(x=STATE,fill = STATE))+
geom_bar()+
theme(axis.text.x = element_text(angle = 90))+
labs(title = "State wise count")
ggplot(auto.claims,aes(x=CLASS,fill = CLASS))+
geom_bar()+
theme(axis.text.x = element_text(angle = 90))+
labs(title = "Class wise count")
gender = auto.claims %>% group_by(GENDER) %>% summarise(count = round(n()*100/nrow(auto.claims),2))
pie(gender$count,
labels = c(paste("Females - ",gender$count[1],"%"),paste("Males - ",gender$count[2],"%")),
main = "Males V/S Females",
col = c("light pink","sky blue")
)
auto.claims$agebin = cut(auto.claims$AGE,c(0,59,69,79,89,Inf),labels = c("50-59","60-69","70-79","80-89","90+"))
ggplot(auto.claims,aes(x=agebin,fill = agebin))+
geom_bar()+
labs(title="Age-bin wise count")
ggplot(auto.claims,aes(x="Claim",y=PAID))+
geom_boxplot()+
labs(title= "Box-Plot of Claim Paid")
From the above boxplot we can see that the there are a lot of outliers for the amount of claim paid, let's see how it looks like after remvoing the outliers
outlier.limit = quantile(auto.claims$PAID,probs = 0.75)+1.5*IQR(auto.claims$PAID)
ggplot(auto.claims,aes(x=PAID))+
geom_histogram(bins = 15,fill = "purple",col = "black")+
scale_x_continuous(limits = c(0,outlier.limit))+
theme_dark()## Warning: Removed 586 rows containing non-finite values (stat_bin).
We observe that Amount of claim is right-skewed
qqnorm(auto.claims$PAID)
Looking the histogram and the QQ-plot of amount of claim PAID, we can see that it is not normally distributed and is highly skewed. Therefore, we will use non-parametric tests in our further analysis.
ggplot(auto.claims,aes(x=STATE,y=PAID,fill = STATE))+
geom_boxplot(show.legend = F)+
scale_y_continuous(limits = c(0,outlier.limit))+
theme(axis.text.x = element_text(angle = 90,hjust = 1))+
ggtitle(label = "State - Wise Box Plot for Amount of Claim Paid",subtitle = "(Excluding Outliers from the main data)")
We can observer that the amount of claim paid varies according to different States, let us now test statisticaly if these differences are significant.
kruskal.test(PAID ~ STATE,data = auto.claims)##
## Kruskal-Wallis rank sum test
##
## data: PAID by STATE
## Kruskal-Wallis chi-squared = 57.798, df = 12, p-value = 5.67e-08
Since p-value is less than 0.05, we will reject our null hypothesis that Claims Paid do not differ according to different States and we'll further perform post-hoc
pairwise.wilcox.test.graph(auto.claims$PAID,auto.claims$STATE)
pairwise.wilcox.test.graph is a function that I created (refer Appendix for syntax). The red area denotes that, statisticaly there is a difference between the two coresspoding States. For example, State 06 differs from State 02,03 and 04 in terms of amount of claim Paid.
We, will follow the above procedure for each variable.
ggplot(auto.claims,aes(x=CLASS,y=PAID,fill = CLASS))+
geom_boxplot(show.legend = F)+
scale_y_continuous(limits = c(0,outlier.limit))+
ggtitle(label = "CLASS - Wise Box Plot for Amount of Claim Paid",subtitle = "(Excluding Outliers from the main data)")## Warning: Removed 586 rows containing non-finite values (stat_boxplot).
kruskal.test(PAID ~ CLASS,data = auto.claims)##
## Kruskal-Wallis rank sum test
##
## data: PAID by CLASS
## Kruskal-Wallis chi-squared = 35.669, df = 17, p-value = 0.005077
Since, the p-value is 0.05, it is worth performing a post hoc just to make sure if any two Classes differ wrt Amount of claim paid
pairwise.wilcox.test.graph(auto.claims$PAID,auto.claims$CLASS)
We, observe that class F7 and C7C differ significantly wrt Amount of claim Paid.
ggplot(auto.claims,aes(x=GENDER,y=PAID,fill = GENDER))+
geom_boxplot()+
scale_y_continuous(limits = c(0,outlier.limit))+
theme(axis.text.x = element_text(angle = 90,hjust = 1))+
ggtitle(label = "GENDER - Wise Box Plot for Amount of Claim Paid",subtitle = "(Excluding Outliers from the main data)")## Warning: Removed 586 rows containing non-finite values (stat_boxplot).
kruskal.test(PAID ~ GENDER,data = auto.claims)##
## Kruskal-Wallis rank sum test
##
## data: PAID by GENDER
## Kruskal-Wallis chi-squared = 2.9465, df = 1, p-value = 0.08607
Since p-value is more than 0.05, we fail to reject our null hypothesis that Claims Paid do not differ according to Gender
ggplot(auto.claims,aes(x=AGE,y=PAID))+
geom_point()+
geom_smooth()## `geom_smooth()` using method = 'gam'
We can see that the amount of claim paid is almost evenly distributed among different ages. Now, can use age bins which will help us analyse age with amount of claim paid even more.
auto.claims %>% group_by(agebin) %>% summarise(median.paid = median(PAID)) %>%
ggplot(aes(x=agebin,y=median.paid,fill = agebin))+
geom_bar(stat = 'identity')
kruskal.test(PAID ~ agebin,data = auto.claims)##
## Kruskal-Wallis rank sum test
##
## data: PAID by agebin
## Kruskal-Wallis chi-squared = 20.157, df = 4, p-value = 0.000465
Since p-value is less than 0.05, we reject our null hypothesis that Claims Paid do not differ according to age.
pairwise.wilcox.test.graph(auto.claims$PAID,auto.claims$agebin)
Since our data has a lot of outliers in our target variable, it is worth analysing how the outliers are distributed among each independent variable.
First, let us look at the percentage of total outliers in our target variable.
## We have 8.65 % of outliers in our targer variable (PAID)
auto.claims %>% group_by(STATE) %>% summarise(n.outliers = length(which(PAID > outlier.limit)),count = n()) %>%
ggplot() +
geom_bar(aes(x=reorder(STATE,-n.outliers),y=count),stat = 'identity',fill =
'white',col='black')+
geom_bar(aes(x=STATE,y=n.outliers),stat = 'identity',fill= "red",col = "black")+
geom_text(aes(x=STATE,y=count,label = round(n.outliers*100/count,2)),stat =
"identity",vjust=-0.2)+
theme_grey()+
theme(axis.text.x = element_text(angle = 90,hjust = 1))+
ggtitle(label = "State wise Claim Amount outlier analysis")+
xlab(label = "STATE")
Now, the above graph has a lot of information, which can be easily understood.
- Each bar is the total count in each state.
- The red bar inside the white bar is the number of outliers in each state.
- The number on the top of each bar is the percentage of outliers in each state.
- Also the States has been re-ordered according in decreasing order of number of outliers.
From the above graph, we can see that even though, the max number of outliers are from STATE 15, but the maximum percentage of outliers is ffrom STATE 12.
We, will make a similar graph for each variable.
auto.claims %>% group_by(CLASS) %>% summarise(n.outliers = length(which(PAID > outlier.limit)),count = n()) %>%
ggplot() +
geom_bar(aes(x=reorder(CLASS,-n.outliers),y=count),stat = 'identity',fill =
'white',col='black')+
geom_bar(aes(x=CLASS,y=n.outliers),stat = 'identity',fill= "red",col = "black")+
geom_text(aes(x=CLASS,y=count,label = round(n.outliers*100/count,2)),stat =
"identity",vjust=-0.2)+
theme_grey()+
theme(axis.text.x = element_text(angle = 90,hjust = 1))+
ggtitle(label = "CLASS wise Claim Amount outlier analysis")+
xlab(label = "CLASS")
CLASS C72 stands at 8th position in total number of outliers, but it has the highest percentage of outliers in CLASS. Also, CLASS F7 has no outliers.
auto.claims %>% group_by(GENDER) %>% summarise(n.outliers = length(which(PAID > outlier.limit)),count = n()) %>%
ggplot() +
geom_bar(aes(x=reorder(GENDER,-n.outliers),y=count),stat = 'identity',fill =
'white',col='black')+
geom_bar(aes(x=GENDER,y=n.outliers),stat = 'identity',fill= "red",col = "black")+
geom_text(aes(x=GENDER,y=count,label = round(n.outliers*100/count,2)),stat =
"identity",vjust=-0.2)+
theme_grey()+
theme(axis.text.x = element_text(angle = 90,hjust = 1))+
ggtitle(label = "GENDER wise Claim Amount outlier analysis")+
xlab(label = "GENDER")
Even though we the count of Males is more, the outlier percentage is almost equal for both the Genders.
auto.claims %>% group_by(agebin) %>% summarise(n.outliers = length(which(PAID > outlier.limit)),count = n()) %>%
ggplot() +
geom_bar(aes(x=reorder(agebin,-n.outliers),y=count),stat = 'identity',fill =
'white',col='black')+
geom_bar(aes(x=agebin,y=n.outliers),stat = 'identity',fill= "red",col = "black")+
geom_text(aes(x=agebin,y=count,label = round(n.outliers*100/count,2)),stat =
"identity",vjust=-0.2)+
theme_grey()
Even though, the count for the age group 90+ is least. It has the highest percentage of outliers.
auto.claims %>% group_by(STATE,CLASS) %>% summarise(perc.outliers =length(which(PAID > outlier.limit))*100/n()) %>%
ggplot(aes(x=STATE,y=CLASS,fill=perc.outliers))+
geom_tile(col="black")+
scale_fill_gradient(low="white",high="dark orange")+
theme_classic()+
theme(axis.text.x = element_text(angle = 90))+
ggtitle(label = "STATE+ CLASS wise percentage of Claim Amount outliers" )
The outliers are not evenly distributed among States and Class taken together. We can see that Classes have different number of outliers according to different STATE.
auto.claims %>% group_by(STATE,GENDER) %>% summarise(perc.outliers =length(which(PAID > outlier.limit))*100/n()) %>%
ggplot(aes(x=STATE,y=GENDER,fill=perc.outliers))+
geom_tile(col="black")+
scale_fill_gradient(low="white",high="dark orange")+
theme_classic()+
theme(axis.text.x = element_text(angle = 90))+
ggtitle(label = "STATE+ GENDER wise percentage of Claim Amount outliers" )
Females of STATE 11 has both the highest number of outliers and Males of the same STATE has the lowest number of outliers.
auto.claims %>% group_by(STATE,agebin) %>% summarise(perc.outliers =length(which(PAID > outlier.limit))*100/n()) %>%
ggplot(aes(x=STATE,y=agebin,fill=perc.outliers))+
geom_tile(col="black")+
scale_fill_gradient(low="white",high="dark orange")+
theme_classic()+
theme(axis.text.x = element_text(angle = 90))+
ggtitle(label = "STATE+ agebin wise percentage of Claim Amount outliers" )
From the above and the previous heat map, we observer that Females with age between 80-89 who are from STATE 11 have higher percentage of outliers.
auto.claims %>% group_by(CLASS,GENDER) %>% summarise(perc.outliers =length(which(PAID > outlier.limit))*100/n()) %>%
ggplot(aes(x=CLASS,y=GENDER,fill=perc.outliers))+
geom_tile(col="black")+
scale_fill_gradient(low="white",high="dark orange")+
theme_classic()+
theme(axis.text.x = element_text(angle = 90))+
ggtitle(label = "CLASS + GENDER wise percentage of Claim Amount outliers" )
auto.claims %>% group_by(CLASS,agebin) %>% summarise(perc.outliers =length(which(PAID > outlier.limit))*100/n()) %>%
ggplot(aes(x=agebin,y=CLASS,fill=perc.outliers))+
geom_tile(col="black")+
scale_fill_gradient(low="white",high="dark orange")+
theme_classic()+
theme(axis.text.x = element_text(angle = 90))+
ggtitle(label = "CLASS + AGE BIN wise percentage of Claim Amount outliers" )
auto.claims %>% group_by(GENDER,agebin) %>% summarise(perc.outliers =length(which(PAID > outlier.limit))*100/n()) %>%
ggplot(aes(x=GENDER,y=agebin,fill=perc.outliers))+
geom_tile(col="black")+
scale_fill_gradient(low="white",high="dark orange")+
theme_classic()+
theme(axis.text.x = element_text(angle = 90))+
ggtitle(label = "GENDER + AGE BIN wise percentage of Claim Amount outliers" )
Females in age group 90+ have high percentage of Claim Amount outliers,while Males in age group 70-79 have a lower percentage of Claim Amount outliers
auto.claims %>% group_by(STATE,CLASS) %>% summarise(count = n(),median = median(PAID)) %>%
ggplot(aes(x=STATE,y=CLASS,fill=count))+
geom_tile(col="black")+
scale_fill_gradient(low="sky blue",high="purple")+
theme(axis.text.x = element_text(angle = 90))+
ggtitle(label = "State + Class wise count")
The CLASS wise data according to each STATE looks evenly distributed for majority of STATES, The noticiable States are State 11 which does not contain more than half of the Classes, and State 15 which has an uneven distribution of classes
auto.claims %>% group_by(STATE,CLASS) %>% summarise(median = median(PAID)) %>%
ggplot(aes(x=STATE,y=CLASS,fill=median))+
geom_tile(col="black")+
scale_fill_gradient(low="white",high="red")+
theme(axis.text.x = element_text(angle = 90))+
ggtitle(label = "State + Class wise median Amount of claim paid")
The class F6 from State06 has the highest median Amount of claim PAID
auto.claims %>% group_by(STATE,CLASS) %>% summarise(m2fratio = length(which(GENDER == 'M'))/length(which(GENDER=='F'))) %>%
ggplot(aes(x=STATE,y=CLASS,fill = m2fratio))+
geom_tile(col = "black")+
scale_fill_gradient(low = "white",high="orange")+
theme(axis.text.x = element_text(angle = 90))+
ggtitle(label = "STATE + CLASS wise Male to female ratio" )
The complete white area dipicts there are no males in the intersection of STATE and CLASS and the grey Area shows there are no females. One interesting observation is that in STATE 11 we have either Only Males or Only Females according to different Classes
auto.claims %>% group_by(STATE,GENDER) %>% summarise(count = n(),Median.paid = median(PAID)) %>%
ggplot(aes(x=STATE,y=GENDER,col= Median.paid))+
geom_point(aes(size = count))+
scale_colour_gradient(low="sky blue",high="purple")+
scale_size(range = c(5,15))+
theme_minimal()+
theme(axis.text.x = element_text(angle = 90))+
ggtitle(label = "STATE + GENDER wise count and Median of Claim Amount Paid")
The size of the point dipicts the count of Gender according to different States. The colour of the point dipicts the Median of Claim amount Paid.
We can see that the gender ratio is almost equal amongst the States
ggplot(auto.claims[auto.claims$PAID<outlier.limit,])+
geom_point(aes(x=AGE,y=PAID,col = STATE),show.legend = F)+
facet_wrap(~STATE)+
ggtitle(label = "Age wise spread of Amount of Claim paid in each state",subtitle = "(Excluding outliers)")
auto.claims %>% group_by(STATE,agebin) %>% summarise(count = n(),median.paid = median(PAID)) %>%
ggplot(aes(x=STATE,y=agebin,col= median.paid))+
geom_point(aes(size = count))+
scale_colour_gradient(low="sky blue",high="purple")+
scale_size(range = c(5,15))+
theme_minimal()+
theme(axis.text.x = element_text(angle = 90))+
ggtitle(label = "STATE + age wise count and Median of Claim Amount Paid")
The size of the point dipicts the count of different according to different States. The colour of the point dipicts the Median of Claim amount Paid.
We can see that in each State the age group 50-59 has the highest count and the count decreases as the age increases and The Median Amount of claim paid is similar for each agebin amongst different STATE.
auto.claims %>% group_by(CLASS,GENDER) %>% summarise(median.paid = median(PAID)) %>%
ggplot(aes(x=CLASS,y=GENDER,fill = median.paid))+
geom_tile()+
scale_fill_continuous(low = 'yellow',high = 'purple')+
ggtitle(label = "CLASS + GENDER wise Median of Claim Amount Paid")
ggplot(auto.claims[auto.claims$PAID<outlier.limit,])+
geom_point(aes(x=AGE,y=PAID,col = CLASS),show.legend = F)+
facet_wrap(~CLASS)+
ggtitle(label = "Age wise spread of Amount of Claim paid in each CLASS",subtitle = "(Excluding outliers)")
We see that most of the Classes have a very specific age group for example Class F6 does not have any observation below the age 70, similar observations can be made about other classes as well. It is worth looking at the box plot of age according to different Classes
ggplot(auto.claims,aes(x=CLASS,y=AGE,fill = CLASS))+
geom_boxplot(show.legend = F)+
ggtitle("Class wise Boxplot of age")
We observe that different classes have different spread of age groups.
ggplot(auto.claims,aes(x=GENDER,y=AGE,fill = GENDER))+
geom_boxplot()
We observe that both the Genders have similar spread of age groups
auto.claims %>% group_by(GENDER,agebin) %>% summarise(median.paid = median(PAID)) %>%
ggplot(aes(x=GENDER,y=agebin,fill= median.paid))+
geom_tile()+
scale_fill_continuous(low = 'yellow',high = 'purple')+
ggtitle("GENDER + Age wise Median of Claim Amount Paid")
Females in the age group 90+ have higher Median of Claim Amount Paid
auto.claims%>% group_by(STATE,CLASS) %>% summarise(avg.age = mean(AGE)) %>%
ggplot(aes(x=STATE,y=CLASS,fill= avg.age))+
geom_tile()+
scale_fill_gradient(low="sky blue",high="purple")+
theme(axis.text.x = element_text(angle = 90))+
ggtitle("STATE + CLASS wise average Age")
ggplot(auto.claims)+
geom_boxplot(aes(x=STATE,y=PAID,fill = STATE),show.legend = F)+
facet_wrap(~GENDER)+
theme(axis.text.x = element_text(angle = 90))+
scale_y_continuous(limits = c(0,outlier.limit))+
ggtitle("STATE wise boxpot of Claim Amount Paid wrt Gender",subtitle = "(Excluding outliers)")## Warning: Removed 586 rows containing non-finite values (stat_boxplot).
ggplot(auto.claims)+
geom_boxplot(aes(x=STATE,y=AGE,fill = STATE),show.legend = F)+
facet_wrap(~GENDER)+
theme(axis.text.x = element_text(angle = 90))+
ggtitle("STATE wise boxpot of Age wrt Gender")
auto.claims %>% group_by(CLASS) %>% summarise(m2fratio = length(which(GENDER=='M'))/length(which(GENDER == 'F'))) %>%
ggplot()+
geom_bar(aes(x=reorder(CLASS,-m2fratio),y=m2fratio,fill = CLASS),stat = 'identity',show.legend = F)+
ggtitle("Class wise male to female ratio")+
xlab("CLASS")
ggplot(auto.claims,aes(x=CLASS,y=AGE,fill = CLASS))+
geom_boxplot(show.legend = F)+
facet_wrap(~GENDER)+
theme(axis.text.x = element_text(angle = 90))+
ggtitle("CLASS wise boxpot of Age wrt Gender")
auto.claims %>% group_by(agebin) %>% summarise(m2fratio = length(which(GENDER=='M'))/length(which(GENDER == 'F'))) %>%
ggplot()+
geom_bar(aes(x=reorder(agebin,-m2fratio),y=m2fratio,fill = agebin),stat = 'identity',show.legend = F)+
ggtitle("AGE wise male to female ratio")+
xlab("AGE")
Here we are using XGboost to train our model.
library(Matrix)
auto.claims <- auto.claims[-6]
sparse_matrix_full = sparse.model.matrix(PAID~.-1, data = auto.claims)
xgb_ds <- xgb.DMatrix(sparse_matrix_full,label = auto.claims$PAID)
params <- list(booster = "gbtree", objective = "reg:linear", eta= 0.01, gamma=1, max_depth=6, min_child_weight=2, subsample=0.8, colsample_bytree=0.75, eval_metric = 'rmse')
set.seed(123)
xgbcv <- xgb.cv(params = params, data = xgb_ds, nrounds = 1000, nfold = 3, showsd = T, stratified = T, maximize = F)## [1] train-rmse:3218.381754+80.654186 test-rmse:3216.172607+161.341806
## [2] train-rmse:3207.523681+80.871277 test-rmse:3205.883301+161.783815
## [3] train-rmse:3196.644938+80.900379 test-rmse:3195.693604+162.462277
## [4] train-rmse:3186.328369+81.011947 test-rmse:3185.639079+162.985754
## [5] train-rmse:3175.781901+81.155888 test-rmse:3175.808024+163.432456
## [6] train-rmse:3165.527344+81.155987 test-rmse:3166.205566+163.887892
## [7] train-rmse:3155.537191+81.110989 test-rmse:3156.694173+164.378820
## [8] train-rmse:3145.811768+81.136410 test-rmse:3147.358968+164.789240
## [9] train-rmse:3136.137939+81.113181 test-rmse:3138.118245+165.206686
## [10] train-rmse:3126.104167+81.187183 test-rmse:3129.085205+165.664721
## [11] train-rmse:3116.979167+81.557468 test-rmse:3120.336670+166.043833
## [12] train-rmse:3108.189697+81.666950 test-rmse:3111.797282+166.464920
## [13] train-rmse:3098.777344+81.825394 test-rmse:3103.243490+166.865129
## [14] train-rmse:3089.822266+82.010729 test-rmse:3094.656494+167.381302
## [15] train-rmse:3080.607340+82.013042 test-rmse:3086.501546+167.880152
## [16] train-rmse:3071.902018+81.924153 test-rmse:3078.222494+168.368050
## [17] train-rmse:3062.767008+82.007005 test-rmse:3070.176188+168.775163
## [18] train-rmse:3054.373861+81.863993 test-rmse:3062.378988+169.178851
## [19] train-rmse:3046.020264+81.650563 test-rmse:3054.526449+169.616141
## [20] train-rmse:3037.969157+81.662717 test-rmse:3046.923665+169.868855
## [21] train-rmse:3029.416829+81.481889 test-rmse:3039.563721+170.368448
## [22] train-rmse:3021.353597+81.765472 test-rmse:3032.296875+170.630537
## [23] train-rmse:3013.575358+81.916109 test-rmse:3025.027262+171.098806
## [24] train-rmse:3005.658610+81.911112 test-rmse:3017.897298+171.525150
## [25] train-rmse:2998.041097+82.171794 test-rmse:3011.117187+171.958136
## [26] train-rmse:2990.193115+82.131131 test-rmse:3004.372965+172.251530
## [27] train-rmse:2983.034098+82.385086 test-rmse:2997.667236+172.621827
## [28] train-rmse:2975.920980+82.389807 test-rmse:2991.137777+173.009262
## [29] train-rmse:2968.605794+82.452998 test-rmse:2984.681071+173.282142
## [30] train-rmse:2961.633789+82.621577 test-rmse:2978.284424+173.512279
## [31] train-rmse:2954.341878+82.392764 test-rmse:2972.155355+173.722742
## [32] train-rmse:2947.566895+82.508985 test-rmse:2966.000244+173.994108
## [33] train-rmse:2941.048584+82.668326 test-rmse:2959.954753+174.270153
## [34] train-rmse:2934.402344+82.649788 test-rmse:2954.242431+174.597574
## [35] train-rmse:2927.858887+82.973649 test-rmse:2948.499349+174.912891
## [36] train-rmse:2921.043050+82.995120 test-rmse:2942.767822+175.207312
## [37] train-rmse:2914.553548+83.374590 test-rmse:2937.219075+175.675573
## [38] train-rmse:2908.213786+83.206486 test-rmse:2931.712646+175.898049
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## [668] train-rmse:2369.181722+61.066890 test-rmse:2720.636719+196.981689
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## [671] train-rmse:2368.671794+61.070509 test-rmse:2721.004151+197.135544
## [672] train-rmse:2368.566976+61.108471 test-rmse:2721.082031+197.188266
## [673] train-rmse:2368.131348+60.964772 test-rmse:2721.325602+197.178699
## [674] train-rmse:2367.946777+61.059254 test-rmse:2721.462647+197.252428
## [675] train-rmse:2367.827881+61.040356 test-rmse:2721.548177+197.283418
## [676] train-rmse:2367.596110+61.128911 test-rmse:2721.623942+197.317173
## [677] train-rmse:2367.395345+61.200278 test-rmse:2721.689372+197.360242
## [678] train-rmse:2367.168620+61.275805 test-rmse:2721.818115+197.407132
## [679] train-rmse:2367.068359+61.329285 test-rmse:2721.845866+197.401856
## [680] train-rmse:2366.887858+61.414740 test-rmse:2721.957601+197.457165
## [681] train-rmse:2366.667318+61.418536 test-rmse:2722.049316+197.477316
## [682] train-rmse:2366.535075+61.429420 test-rmse:2722.086426+197.562142
## [683] train-rmse:2366.375976+61.458555 test-rmse:2722.139974+197.564865
## [684] train-rmse:2366.203776+61.305634 test-rmse:2722.179851+197.572542
## [685] train-rmse:2366.020671+61.250152 test-rmse:2721.969645+197.515101
## [686] train-rmse:2365.664307+61.182000 test-rmse:2722.179281+197.510266
## [687] train-rmse:2365.482992+60.998429 test-rmse:2722.256673+197.432121
## [688] train-rmse:2365.319010+61.055391 test-rmse:2722.376058+197.442604
## [689] train-rmse:2364.929525+60.817935 test-rmse:2722.501790+197.392905
## [690] train-rmse:2364.736898+60.773781 test-rmse:2722.736002+197.425661
## [691] train-rmse:2364.439372+60.836040 test-rmse:2722.660075+197.433070
## [692] train-rmse:2364.314860+60.927836 test-rmse:2722.592773+197.466085
## [693] train-rmse:2363.875163+61.064379 test-rmse:2722.782959+197.474566
## [694] train-rmse:2363.617920+61.088650 test-rmse:2722.930908+197.479918
## [695] train-rmse:2363.533854+61.053412 test-rmse:2722.977865+197.528392
## [696] train-rmse:2363.315023+61.021670 test-rmse:2723.059001+197.595221
## [697] train-rmse:2363.157633+60.980991 test-rmse:2723.150146+197.641647
## [698] train-rmse:2363.032878+60.922302 test-rmse:2723.167643+197.668458
## [699] train-rmse:2362.813314+60.859186 test-rmse:2723.046550+197.702531
## [700] train-rmse:2362.462565+60.639741 test-rmse:2723.106690+197.653014
## [701] train-rmse:2362.148681+60.529218 test-rmse:2723.289877+197.702223
## [702] train-rmse:2361.776367+60.408885 test-rmse:2723.489584+197.626004
## [703] train-rmse:2361.600179+60.234287 test-rmse:2723.625976+197.534543
## [704] train-rmse:2361.194336+60.275479 test-rmse:2723.861898+197.600958
## [705] train-rmse:2360.983643+60.274444 test-rmse:2723.869548+197.645705
## [706] train-rmse:2360.746745+60.256865 test-rmse:2723.934896+197.665370
## [707] train-rmse:2360.567383+60.256071 test-rmse:2724.062744+197.695028
## [708] train-rmse:2360.403972+60.294192 test-rmse:2724.123210+197.702178
## [709] train-rmse:2360.358643+60.315390 test-rmse:2724.194824+197.774238
## [710] train-rmse:2360.090251+60.334546 test-rmse:2724.392008+197.853104
## [711] train-rmse:2359.899658+60.185024 test-rmse:2724.544190+197.788128
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## [713] train-rmse:2359.378337+60.069722 test-rmse:2724.764648+197.727666
## [714] train-rmse:2359.291504+60.080732 test-rmse:2724.747640+197.673827
## [715] train-rmse:2358.924805+60.080651 test-rmse:2724.692464+197.674683
## [716] train-rmse:2358.698730+60.071734 test-rmse:2724.859782+197.657319
## [717] train-rmse:2358.519043+59.928374 test-rmse:2725.054036+197.618180
## [718] train-rmse:2358.393554+59.936040 test-rmse:2725.136963+197.716537
## [719] train-rmse:2358.102458+59.958757 test-rmse:2725.279704+197.755934
## [720] train-rmse:2357.940755+60.013442 test-rmse:2725.210937+197.741177
## [721] train-rmse:2357.872640+60.007163 test-rmse:2725.234456+197.720480
## [722] train-rmse:2357.594645+59.883746 test-rmse:2725.386881+197.644504
## [723] train-rmse:2357.482747+59.922685 test-rmse:2725.499430+197.698390
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## [725] train-rmse:2357.110026+60.005976 test-rmse:2725.831706+197.872932
## [726] train-rmse:2356.625977+60.170027 test-rmse:2726.144531+197.980536
## [727] train-rmse:2356.380127+60.077936 test-rmse:2726.134277+197.907139
## [728] train-rmse:2356.175049+60.174115 test-rmse:2726.319824+198.033929
## [729] train-rmse:2355.784505+59.943976 test-rmse:2726.537435+198.187723
## [730] train-rmse:2355.642090+59.982872 test-rmse:2726.588623+198.192763
## [731] train-rmse:2355.448893+60.129118 test-rmse:2726.432698+198.128662
## [732] train-rmse:2355.154053+60.105420 test-rmse:2726.483806+198.073202
## [733] train-rmse:2354.856445+60.165776 test-rmse:2726.687663+198.154110
## [734] train-rmse:2354.510824+60.078984 test-rmse:2726.854818+198.160867
## [735] train-rmse:2354.264323+59.964868 test-rmse:2726.867676+198.149878
## [736] train-rmse:2353.943848+59.815368 test-rmse:2727.070557+198.064267
## [737] train-rmse:2353.851237+59.788358 test-rmse:2726.986817+198.045974
## [738] train-rmse:2353.688232+59.672914 test-rmse:2727.093831+197.907943
## [739] train-rmse:2353.473551+59.613202 test-rmse:2727.192790+197.961118
## [740] train-rmse:2353.159017+59.555940 test-rmse:2727.364665+198.006608
## [741] train-rmse:2352.753093+59.235415 test-rmse:2727.339681+198.158309
## [742] train-rmse:2352.539713+59.219297 test-rmse:2727.452799+198.148900
## [743] train-rmse:2352.315430+59.180774 test-rmse:2727.650716+198.109994
## [744] train-rmse:2352.138590+59.305382 test-rmse:2727.776286+198.247573
## [745] train-rmse:2351.910889+59.402242 test-rmse:2727.904216+198.315121
## [746] train-rmse:2351.497966+59.201353 test-rmse:2728.266357+198.240301
## [747] train-rmse:2351.399414+59.105873 test-rmse:2728.298828+198.203327
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## [749] train-rmse:2350.882487+59.248520 test-rmse:2728.572835+198.281640
## [750] train-rmse:2350.559652+59.413093 test-rmse:2728.845378+198.401764
## [751] train-rmse:2350.165446+59.213139 test-rmse:2728.876221+198.296954
## [752] train-rmse:2350.077149+59.187667 test-rmse:2728.718262+198.280889
## [753] train-rmse:2349.817383+59.159173 test-rmse:2728.900797+198.299035
## [754] train-rmse:2349.716309+59.077467 test-rmse:2728.891276+198.347083
## [755] train-rmse:2349.579590+59.053763 test-rmse:2728.930013+198.394034
## [756] train-rmse:2349.392008+59.165201 test-rmse:2728.837891+198.399823
## [757] train-rmse:2349.311849+59.140011 test-rmse:2728.806152+198.416485
## [758] train-rmse:2349.028401+59.313842 test-rmse:2728.866618+198.415327
## [759] train-rmse:2348.589030+59.093076 test-rmse:2729.195475+198.370897
## [760] train-rmse:2348.182780+59.049245 test-rmse:2729.314290+198.262825
## [761] train-rmse:2347.888997+58.945551 test-rmse:2729.426595+198.197818
## [762] train-rmse:2347.549072+59.036612 test-rmse:2729.401449+198.136758
## [763] train-rmse:2347.266601+59.191212 test-rmse:2729.523193+198.165584
## [764] train-rmse:2346.930013+59.221602 test-rmse:2729.784098+198.231628
## [765] train-rmse:2346.665853+59.231289 test-rmse:2729.934977+198.318347
## [766] train-rmse:2346.329509+59.261728 test-rmse:2730.131673+198.344217
## [767] train-rmse:2346.179362+59.226657 test-rmse:2730.159912+198.421293
## [768] train-rmse:2346.047119+59.232782 test-rmse:2730.170491+198.481374
## [769] train-rmse:2345.754476+59.084470 test-rmse:2730.243327+198.426086
## [770] train-rmse:2345.465820+59.171452 test-rmse:2730.400472+198.363095
## [771] train-rmse:2345.290121+59.258047 test-rmse:2730.523600+198.435949
## [772] train-rmse:2345.134115+59.306199 test-rmse:2730.349609+198.437091
## [773] train-rmse:2344.998535+59.408767 test-rmse:2730.388265+198.454728
## [774] train-rmse:2344.957113+59.417425 test-rmse:2730.394613+198.441240
## [775] train-rmse:2344.735433+59.327404 test-rmse:2730.372640+198.448795
## [776] train-rmse:2344.495280+59.196910 test-rmse:2730.343425+198.488990
## [777] train-rmse:2344.116944+59.023533 test-rmse:2730.622070+198.406727
## [778] train-rmse:2343.888590+58.896415 test-rmse:2730.832357+198.439903
## [779] train-rmse:2343.496094+58.618712 test-rmse:2730.818522+198.591699
## [780] train-rmse:2343.171956+58.659264 test-rmse:2730.877848+198.615008
## [781] train-rmse:2342.818603+58.788955 test-rmse:2731.097168+198.672357
## [782] train-rmse:2342.612060+58.704234 test-rmse:2731.165772+198.547520
## [783] train-rmse:2342.260905+58.560086 test-rmse:2731.579834+198.414434
## [784] train-rmse:2342.081462+58.535645 test-rmse:2731.685791+198.421390
## [785] train-rmse:2341.832519+58.521763 test-rmse:2731.864990+198.488713
## [786] train-rmse:2341.730631+58.509305 test-rmse:2731.953125+198.434255
## [787] train-rmse:2341.497477+58.356023 test-rmse:2731.904460+198.372429
## [788] train-rmse:2341.270101+58.310000 test-rmse:2732.057536+198.237376
## [789] train-rmse:2340.960612+58.272601 test-rmse:2732.106527+198.209514
## [790] train-rmse:2340.803385+58.238008 test-rmse:2732.160726+198.213972
## [791] train-rmse:2340.526855+58.131257 test-rmse:2732.373616+198.109130
## [792] train-rmse:2340.439290+58.070710 test-rmse:2732.407552+198.138327
## [793] train-rmse:2340.178060+57.975682 test-rmse:2732.835286+198.101730
## [794] train-rmse:2339.981689+57.839581 test-rmse:2732.857341+198.095781
## [795] train-rmse:2339.779623+57.678608 test-rmse:2733.017741+198.004138
## [796] train-rmse:2339.680339+57.718690 test-rmse:2733.057943+198.032301
## [797] train-rmse:2339.415365+57.686470 test-rmse:2732.942301+198.026318
## [798] train-rmse:2339.205485+57.714741 test-rmse:2733.152018+198.066888
## [799] train-rmse:2338.949625+57.544694 test-rmse:2733.310384+197.950654
## [800] train-rmse:2338.570719+57.496505 test-rmse:2733.289958+197.834807
## [801] train-rmse:2338.431559+57.523825 test-rmse:2733.488282+197.875220
## [802] train-rmse:2338.282308+57.469683 test-rmse:2733.512614+197.852910
## [803] train-rmse:2338.174317+57.406190 test-rmse:2733.364502+197.894031
## [804] train-rmse:2337.939860+57.504074 test-rmse:2733.562663+197.954558
## [805] train-rmse:2337.692139+57.654750 test-rmse:2733.758871+198.034169
## [806] train-rmse:2337.455810+57.757470 test-rmse:2733.832194+198.041853
## [807] train-rmse:2337.079590+57.843040 test-rmse:2733.870687+198.063042
## [808] train-rmse:2336.784017+57.974014 test-rmse:2734.066081+198.120933
## [809] train-rmse:2336.606120+57.957039 test-rmse:2734.101969+198.100873
## [810] train-rmse:2336.418213+57.906029 test-rmse:2734.193929+198.062376
## [811] train-rmse:2336.332520+57.956716 test-rmse:2734.267741+198.051118
## [812] train-rmse:2336.138265+58.057483 test-rmse:2734.547689+198.206394
## [813] train-rmse:2335.892090+58.026767 test-rmse:2734.767659+198.097453
## [814] train-rmse:2335.618408+57.809475 test-rmse:2735.078695+197.813406
## [815] train-rmse:2335.440674+57.877148 test-rmse:2735.119059+197.855460
## [816] train-rmse:2335.059408+57.898337 test-rmse:2735.363932+197.834366
## [817] train-rmse:2334.929199+57.815278 test-rmse:2735.401123+197.702689
## [818] train-rmse:2334.801188+57.703216 test-rmse:2735.617025+197.503436
## [819] train-rmse:2334.657715+57.728914 test-rmse:2735.592611+197.498905
## [820] train-rmse:2334.444499+57.733290 test-rmse:2735.734050+197.475797
## [821] train-rmse:2334.373861+57.766988 test-rmse:2735.784342+197.442772
## [822] train-rmse:2334.289062+57.794340 test-rmse:2735.849446+197.510933
## [823] train-rmse:2334.109701+57.728159 test-rmse:2735.979655+197.473796
## [824] train-rmse:2333.828288+57.714028 test-rmse:2736.204427+197.471792
## [825] train-rmse:2333.638184+57.695493 test-rmse:2736.193929+197.344213
## [826] train-rmse:2333.410563+57.746902 test-rmse:2736.215657+197.266102
## [827] train-rmse:2333.231038+57.583836 test-rmse:2736.328939+197.127866
## [828] train-rmse:2333.093424+57.650533 test-rmse:2736.336344+197.155555
## [829] train-rmse:2332.858154+57.621795 test-rmse:2736.289388+197.072991
## [830] train-rmse:2332.845133+57.629234 test-rmse:2736.335368+197.055984
## [831] train-rmse:2332.618571+57.583931 test-rmse:2736.500407+196.976796
## [832] train-rmse:2332.325277+57.411370 test-rmse:2736.559408+197.007647
## [833] train-rmse:2332.178630+57.512211 test-rmse:2736.653158+197.018794
## [834] train-rmse:2331.999512+57.607226 test-rmse:2736.689128+197.051662
## [835] train-rmse:2331.864421+57.678581 test-rmse:2736.803060+197.129575
## [836] train-rmse:2331.772379+57.645388 test-rmse:2737.057373+197.146421
## [837] train-rmse:2331.568604+57.568887 test-rmse:2737.200765+197.143040
## [838] train-rmse:2331.452474+57.522734 test-rmse:2737.233073+197.166685
## [839] train-rmse:2331.340576+57.474236 test-rmse:2737.203206+197.089111
## [840] train-rmse:2331.156169+57.292821 test-rmse:2737.194336+197.019698
## [841] train-rmse:2330.875732+57.383054 test-rmse:2737.326009+197.041331
## [842] train-rmse:2330.567139+57.364524 test-rmse:2737.542643+197.134457
## [843] train-rmse:2330.389486+57.386029 test-rmse:2737.651937+197.168991
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## [845] train-rmse:2330.109538+57.297642 test-rmse:2737.739909+197.168016
## [846] train-rmse:2330.011312+57.305196 test-rmse:2737.720378+197.173379
## [847] train-rmse:2329.801026+57.325447 test-rmse:2737.609863+197.234094
## [848] train-rmse:2329.477213+57.223341 test-rmse:2737.821208+197.310891
## [849] train-rmse:2329.238607+57.333186 test-rmse:2737.958008+197.451517
## [850] train-rmse:2328.926351+57.085792 test-rmse:2738.260335+197.181015
## [851] train-rmse:2328.776123+57.103052 test-rmse:2738.264974+197.127291
## [852] train-rmse:2328.619384+57.162612 test-rmse:2738.328450+197.181746
## [853] train-rmse:2328.410726+57.021556 test-rmse:2738.362630+197.178259
## [854] train-rmse:2328.152913+57.009228 test-rmse:2738.420817+197.139225
## [855] train-rmse:2328.031576+57.018021 test-rmse:2738.321045+197.237045
## [856] train-rmse:2327.924967+57.061087 test-rmse:2738.444499+197.277561
## [857] train-rmse:2327.631917+57.049837 test-rmse:2738.525635+197.381054
## [858] train-rmse:2327.522868+56.977393 test-rmse:2738.568766+197.366255
## [859] train-rmse:2327.447998+57.012453 test-rmse:2738.452556+197.305832
## [860] train-rmse:2327.242106+56.983857 test-rmse:2738.554362+197.327531
## [861] train-rmse:2327.119873+57.000063 test-rmse:2738.478109+197.327443
## [862] train-rmse:2326.940023+57.085949 test-rmse:2738.492350+197.294445
## [863] train-rmse:2326.794271+57.027377 test-rmse:2738.510010+197.195307
## [864] train-rmse:2326.655925+57.100003 test-rmse:2738.599121+197.208729
## [865] train-rmse:2326.402018+57.139853 test-rmse:2738.638753+197.198343
## [866] train-rmse:2326.053385+56.872911 test-rmse:2738.672282+196.984910
## [867] train-rmse:2325.936930+56.977842 test-rmse:2738.755534+197.077949
## [868] train-rmse:2325.871419+57.023552 test-rmse:2738.775879+197.080318
## [869] train-rmse:2325.688233+57.130362 test-rmse:2738.921468+197.200503
## [870] train-rmse:2325.454264+57.256227 test-rmse:2739.150879+197.289515
## [871] train-rmse:2325.329264+57.203611 test-rmse:2739.214518+197.280189
## [872] train-rmse:2325.150798+57.134784 test-rmse:2739.327393+197.227834
## [873] train-rmse:2324.876221+56.933507 test-rmse:2739.379476+196.869746
## [874] train-rmse:2324.821045+56.963993 test-rmse:2739.389648+196.869514
## [875] train-rmse:2324.686035+56.967833 test-rmse:2739.342611+196.932126
## [876] train-rmse:2324.557536+56.958275 test-rmse:2739.435872+196.973205
## [877] train-rmse:2324.276611+57.072773 test-rmse:2739.447510+196.979473
## [878] train-rmse:2324.030029+57.021152 test-rmse:2739.452962+196.997927
## [879] train-rmse:2323.582927+57.011434 test-rmse:2739.757080+196.784125
## [880] train-rmse:2323.521647+57.058633 test-rmse:2739.819255+196.794219
## [881] train-rmse:2323.273275+57.045216 test-rmse:2739.795736+196.734481
## [882] train-rmse:2322.895915+57.053430 test-rmse:2739.981852+196.767139
## [883] train-rmse:2322.617431+57.059386 test-rmse:2740.234375+196.774234
## [884] train-rmse:2322.540690+57.078498 test-rmse:2740.343913+196.792089
## [885] train-rmse:2322.337809+57.063559 test-rmse:2740.453369+196.792130
## [886] train-rmse:2322.266683+57.057997 test-rmse:2740.540853+196.811999
## [887] train-rmse:2322.168050+57.106928 test-rmse:2740.612793+196.814450
## [888] train-rmse:2321.778483+56.981859 test-rmse:2740.908773+196.755480
## [889] train-rmse:2321.542318+56.917812 test-rmse:2741.169840+196.863228
## [890] train-rmse:2321.317790+56.838564 test-rmse:2741.399251+196.791240
## [891] train-rmse:2321.193522+56.752278 test-rmse:2741.579671+196.748571
## [892] train-rmse:2321.057373+56.803700 test-rmse:2741.473551+196.784237
## [893] train-rmse:2320.739258+56.645829 test-rmse:2741.624756+196.589078
## [894] train-rmse:2320.669678+56.672803 test-rmse:2741.679280+196.597885
## [895] train-rmse:2320.557210+56.723321 test-rmse:2741.539225+196.764555
## [896] train-rmse:2320.401937+56.733955 test-rmse:2741.649577+196.782340
## [897] train-rmse:2320.183024+56.689975 test-rmse:2741.833008+196.776678
## [898] train-rmse:2319.924805+56.761450 test-rmse:2741.969075+196.839557
## [899] train-rmse:2319.770101+56.870439 test-rmse:2742.103271+196.906163
## [900] train-rmse:2319.613770+56.817366 test-rmse:2742.342285+196.892970
## [901] train-rmse:2319.482504+56.852600 test-rmse:2742.398519+196.938578
## [902] train-rmse:2319.387126+56.778373 test-rmse:2742.453939+196.953174
## [903] train-rmse:2319.132406+56.744889 test-rmse:2742.583496+197.008122
## [904] train-rmse:2318.954753+56.850624 test-rmse:2742.669108+197.001614
## [905] train-rmse:2318.850423+56.905594 test-rmse:2742.699951+197.033091
## [906] train-rmse:2318.696696+56.880478 test-rmse:2742.700846+197.054817
## [907] train-rmse:2318.455485+56.956246 test-rmse:2742.975423+197.143303
## [908] train-rmse:2318.301432+56.897496 test-rmse:2743.004313+197.172057
## [909] train-rmse:2318.045817+56.751462 test-rmse:2743.253255+197.006172
## [910] train-rmse:2317.881917+56.734406 test-rmse:2743.363037+197.062331
## [911] train-rmse:2317.671142+56.633540 test-rmse:2743.661784+196.988548
## [912] train-rmse:2317.444580+56.681391 test-rmse:2743.563558+196.920281
## [913] train-rmse:2317.351888+56.698698 test-rmse:2743.638184+196.953423
## [914] train-rmse:2317.315592+56.690838 test-rmse:2743.468587+196.921921
## [915] train-rmse:2317.017659+56.693481 test-rmse:2743.677653+196.863158
## [916] train-rmse:2316.981689+56.693389 test-rmse:2743.760417+196.854009
## [917] train-rmse:2316.787598+56.648339 test-rmse:2743.845296+196.901323
## [918] train-rmse:2316.640950+56.655994 test-rmse:2743.907227+196.914500
## [919] train-rmse:2316.431966+56.752513 test-rmse:2744.113688+196.971099
## [920] train-rmse:2316.326009+56.837034 test-rmse:2744.126546+197.027050
## [921] train-rmse:2316.134277+56.850685 test-rmse:2744.210856+196.998108
## [922] train-rmse:2315.845622+56.800864 test-rmse:2744.588704+196.971411
## [923] train-rmse:2315.617431+56.831544 test-rmse:2744.691732+197.073487
## [924] train-rmse:2315.436117+56.882931 test-rmse:2744.845622+197.117309
## [925] train-rmse:2315.298014+56.830166 test-rmse:2744.881917+197.030988
## [926] train-rmse:2314.998454+56.786647 test-rmse:2745.161377+196.933609
## [927] train-rmse:2314.692464+56.775346 test-rmse:2745.286133+196.881828
## [928] train-rmse:2314.617594+56.781816 test-rmse:2745.352621+196.906362
## [929] train-rmse:2314.395427+56.875577 test-rmse:2745.491455+196.984181
## [930] train-rmse:2314.300456+56.895537 test-rmse:2745.524251+196.987230
## [931] train-rmse:2313.937582+57.079691 test-rmse:2745.721517+197.038410
## [932] train-rmse:2313.671224+57.062882 test-rmse:2745.896077+196.998567
## [933] train-rmse:2313.553711+57.073474 test-rmse:2745.936117+197.009748
## [934] train-rmse:2313.212402+57.195666 test-rmse:2746.032064+197.082120
## [935] train-rmse:2313.073975+57.229729 test-rmse:2745.960938+197.115527
## [936] train-rmse:2312.964599+57.293086 test-rmse:2745.988119+197.130523
## [937] train-rmse:2312.765544+57.204622 test-rmse:2746.077311+197.085138
## [938] train-rmse:2312.640625+57.266547 test-rmse:2746.195069+197.247437
## [939] train-rmse:2312.527425+57.230744 test-rmse:2746.247152+197.273622
## [940] train-rmse:2312.383138+57.175656 test-rmse:2746.338216+197.260451
## [941] train-rmse:2312.272624+57.271787 test-rmse:2746.397379+197.360868
## [942] train-rmse:2312.151774+57.337478 test-rmse:2746.432943+197.333437
## [943] train-rmse:2311.988607+57.418185 test-rmse:2746.552816+197.382238
## [944] train-rmse:2311.772461+57.376602 test-rmse:2746.602783+197.331497
## [945] train-rmse:2311.652262+57.420420 test-rmse:2746.671224+197.325588
## [946] train-rmse:2311.460368+57.443925 test-rmse:2746.752686+197.349953
## [947] train-rmse:2311.412435+57.471757 test-rmse:2746.816569+197.369892
## [948] train-rmse:2311.333170+57.491769 test-rmse:2746.845296+197.385990
## [949] train-rmse:2311.250488+57.461878 test-rmse:2746.674561+197.347667
## [950] train-rmse:2311.110433+57.504798 test-rmse:2746.722982+197.338443
## [951] train-rmse:2310.887858+57.665657 test-rmse:2746.808838+197.405316
## [952] train-rmse:2310.835856+57.649824 test-rmse:2746.840820+197.414879
## [953] train-rmse:2310.647705+57.576735 test-rmse:2746.849609+197.407246
## [954] train-rmse:2310.509114+57.666265 test-rmse:2746.924805+197.435463
## [955] train-rmse:2310.305664+57.661640 test-rmse:2747.000000+197.463989
## [956] train-rmse:2309.999919+57.551090 test-rmse:2747.036947+197.445367
## [957] train-rmse:2309.517822+57.397205 test-rmse:2747.379395+197.275143
## [958] train-rmse:2309.366048+57.452544 test-rmse:2747.290039+197.246130
## [959] train-rmse:2309.069580+57.387003 test-rmse:2747.351400+197.221150
## [960] train-rmse:2308.851155+57.417568 test-rmse:2747.424398+197.209255
## [961] train-rmse:2308.696696+57.347032 test-rmse:2747.517985+197.261102
## [962] train-rmse:2308.585449+57.417921 test-rmse:2747.696126+197.282773
## [963] train-rmse:2308.363363+57.213744 test-rmse:2747.790121+197.204883
## [964] train-rmse:2308.234212+57.253894 test-rmse:2747.844726+197.204711
## [965] train-rmse:2308.116700+57.165273 test-rmse:2747.794433+197.201188
## [966] train-rmse:2307.942546+57.261413 test-rmse:2747.820801+197.210527
## [967] train-rmse:2307.796061+57.290904 test-rmse:2747.913005+197.228205
## [968] train-rmse:2307.565918+57.278054 test-rmse:2747.985107+197.265205
## [969] train-rmse:2307.458008+57.344529 test-rmse:2748.019124+197.311991
## [970] train-rmse:2307.362467+57.256000 test-rmse:2748.050781+197.332187
## [971] train-rmse:2307.235840+57.253950 test-rmse:2748.054850+197.371755
## [972] train-rmse:2307.058431+57.108029 test-rmse:2748.189372+197.199016
## [973] train-rmse:2306.816976+57.286077 test-rmse:2748.375733+197.303053
## [974] train-rmse:2306.715007+57.256207 test-rmse:2748.542074+197.298118
## [975] train-rmse:2306.613851+57.341492 test-rmse:2748.601237+197.329317
## [976] train-rmse:2306.484701+57.368043 test-rmse:2748.594808+197.369308
## [977] train-rmse:2306.364827+57.337348 test-rmse:2748.649333+197.273944
## [978] train-rmse:2306.101237+57.088295 test-rmse:2748.823486+197.036263
## [979] train-rmse:2306.011067+57.119250 test-rmse:2748.843750+197.032236
## [980] train-rmse:2305.787598+57.125276 test-rmse:2749.152751+197.050187
## [981] train-rmse:2305.662272+57.161129 test-rmse:2749.156331+197.059260
## [982] train-rmse:2305.611898+57.159073 test-rmse:2749.172201+197.077278
## [983] train-rmse:2305.447754+57.230180 test-rmse:2749.255697+197.071706
## [984] train-rmse:2305.274089+57.113194 test-rmse:2749.340983+197.042199
## [985] train-rmse:2305.158691+57.179093 test-rmse:2749.450684+197.103984
## [986] train-rmse:2305.070231+57.190822 test-rmse:2749.573324+197.142137
## [987] train-rmse:2304.916830+57.125077 test-rmse:2749.627686+197.150660
## [988] train-rmse:2304.657552+56.949654 test-rmse:2749.815837+197.014086
## [989] train-rmse:2304.416097+57.044861 test-rmse:2749.901937+196.979201
## [990] train-rmse:2304.167643+57.181202 test-rmse:2750.115316+197.045480
## [991] train-rmse:2303.939860+57.197480 test-rmse:2750.101644+197.075239
## [992] train-rmse:2303.829915+57.252889 test-rmse:2750.111654+197.071712
## [993] train-rmse:2303.693441+57.315778 test-rmse:2750.210693+197.097224
## [994] train-rmse:2303.531901+57.375987 test-rmse:2750.358480+197.242961
## [995] train-rmse:2303.413411+57.401726 test-rmse:2750.264730+197.192462
## [996] train-rmse:2303.196940+57.513551 test-rmse:2750.398844+197.303829
## [997] train-rmse:2302.814697+57.256102 test-rmse:2750.516113+197.220664
## [998] train-rmse:2302.612142+57.077761 test-rmse:2750.620605+197.185661
## [999] train-rmse:2302.471191+57.158758 test-rmse:2750.626790+197.142818
## [1000] train-rmse:2302.300781+57.090371 test-rmse:2750.680176+197.148281
min_rmse <- min(xgbcv$evaluation_log$test_rmse_mean)The minimum rmse achieved by our model is 2672.35.
pairwise.wilcox.test.graph = function(x,g){
ph.state = pairwise.wilcox.test(x,g)
ph.table = melt(ph.state$p.value)
ph.table$result = sapply(ph.table$value,
function(x){
ifelse(x<0.05,"Significant Difference","No Significant Difference")
})
ph.table = na.omit(ph.table)
wil.plot <- ggplot(ph.table, aes(x=Var1, y=Var2, fill=result))+
geom_tile(col = "black")+
scale_fill_manual(values = c("green","red"))+
theme_minimal()+
theme(axis.text.x = element_text(angle = 90))
return(wil.plot)
}