Outlier Detection

Datanın daxil edilməsi:

iris

df <- iris[1:4] # yalnız ədədi dəyişənlər seçilir
df

NA

2 mərkəzli klaster qurmaq:

library(DMwR)
k_means <- kmeans(df, centers = 2)
k_means

K-means clustering with 2 clusters of sizes 53, 97

Cluster means:
  Sepal.Length Sepal.Width Petal.Length Petal.Width
1     5.005660    3.369811     1.560377    0.290566
2     6.301031    2.886598     4.958763    1.695876

Clustering vector:
  [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 [36] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2
 [71] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 1 2 2 2 2 2 2
[106] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[141] 2 2 2 2 2 2 2 2 2 2

Within cluster sum of squares by cluster:
[1]  28.55208 123.79588
 (between_SS / total_SS =  77.6 %)

Available components:

[1] "cluster"      "centers"      "totss"        "withinss"    
[5] "tot.withinss" "betweenss"    "size"         "iter"        
[9] "ifault"      

Dəyişənlərin mərkəzləri ilə birlikdə ifadə edilməsi:

center_df <- k_means$centers[k_means$cluster,]
center_df

  Sepal.Length Sepal.Width Petal.Length Petal.Width
1     5.005660    3.369811     1.560377    0.290566
1     5.005660    3.369811     1.560377    0.290566
1     5.005660    3.369811     1.560377    0.290566
1     5.005660    3.369811     1.560377    0.290566
1     5.005660    3.369811     1.560377    0.290566
1     5.005660    3.369811     1.560377    0.290566
1     5.005660    3.369811     1.560377    0.290566
1     5.005660    3.369811     1.560377    0.290566
1     5.005660    3.369811     1.560377    0.290566
1     5.005660    3.369811     1.560377    0.290566
1     5.005660    3.369811     1.560377    0.290566
1     5.005660    3.369811     1.560377    0.290566
1     5.005660    3.369811     1.560377    0.290566
1     5.005660    3.369811     1.560377    0.290566
1     5.005660    3.369811     1.560377    0.290566
1     5.005660    3.369811     1.560377    0.290566
1     5.005660    3.369811     1.560377    0.290566
1     5.005660    3.369811     1.560377    0.290566
1     5.005660    3.369811     1.560377    0.290566
1     5.005660    3.369811     1.560377    0.290566
1     5.005660    3.369811     1.560377    0.290566
1     5.005660    3.369811     1.560377    0.290566
1     5.005660    3.369811     1.560377    0.290566
1     5.005660    3.369811     1.560377    0.290566
1     5.005660    3.369811     1.560377    0.290566
1     5.005660    3.369811     1.560377    0.290566
1     5.005660    3.369811     1.560377    0.290566
1     5.005660    3.369811     1.560377    0.290566
1     5.005660    3.369811     1.560377    0.290566
1     5.005660    3.369811     1.560377    0.290566
1     5.005660    3.369811     1.560377    0.290566
1     5.005660    3.369811     1.560377    0.290566
1     5.005660    3.369811     1.560377    0.290566
1     5.005660    3.369811     1.560377    0.290566
1     5.005660    3.369811     1.560377    0.290566
1     5.005660    3.369811     1.560377    0.290566
1     5.005660    3.369811     1.560377    0.290566
1     5.005660    3.369811     1.560377    0.290566
1     5.005660    3.369811     1.560377    0.290566
1     5.005660    3.369811     1.560377    0.290566
1     5.005660    3.369811     1.560377    0.290566
1     5.005660    3.369811     1.560377    0.290566
1     5.005660    3.369811     1.560377    0.290566
1     5.005660    3.369811     1.560377    0.290566
1     5.005660    3.369811     1.560377    0.290566
1     5.005660    3.369811     1.560377    0.290566
1     5.005660    3.369811     1.560377    0.290566
1     5.005660    3.369811     1.560377    0.290566
1     5.005660    3.369811     1.560377    0.290566
1     5.005660    3.369811     1.560377    0.290566
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
1     5.005660    3.369811     1.560377    0.290566
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
1     5.005660    3.369811     1.560377    0.290566
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
1     5.005660    3.369811     1.560377    0.290566
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876
2     6.301031    2.886598     4.958763    1.695876

Hər bir deyişən üçün uzaqlıqların hesablanması:

distance <- sqrt(rowSums(df, center_df)^2)
distance

  [1] 10.2  9.5  9.4  9.4 10.2 11.4  9.7 10.1  8.9  9.6 10.8 10.0  9.3  8.5
 [15] 11.2 12.0 11.0 10.3 11.5 10.7 10.7 10.7  9.4 10.6 10.3  9.8 10.4 10.4
 [29] 10.2  9.7  9.7 10.7 10.9 11.3  9.7  9.6 10.5 10.0  8.9 10.2 10.1  8.4
 [43]  9.1 10.7 11.2  9.5 10.7  9.4 10.7  9.9 16.3 15.6 16.4 13.1 15.4 14.3
 [57] 15.9 11.6 15.4 13.2 11.5 14.6 13.2 15.1 13.4 15.6 14.6 13.6 14.4 13.1
 [71] 15.7 14.2 15.2 14.8 14.9 15.4 15.8 16.4 14.9 12.8 12.8 12.6 13.6 15.4
 [85] 14.4 15.5 16.0 14.3 14.0 13.3 13.7 15.1 13.6 11.6 13.8 14.1 14.1 14.7
 [99] 11.7 13.9 18.1 15.5 18.1 16.6 17.5 19.3 13.6 18.3 16.8 19.4 16.8 16.3
[113] 17.4 15.2 16.1 17.2 16.8 20.4 19.5 14.7 18.1 15.3 19.2 15.7 17.8 18.2
[127] 15.6 15.8 16.9 17.6 18.2 20.1 17.0 15.7 15.7 19.1 17.7 16.8 15.6 17.5
[141] 17.8 17.4 15.5 18.2 18.2 17.2 15.7 16.7 17.3 15.8

İlk 5 outlier detection indeksləri:

od_index <- order(distance, decreasing = T)[1:5]
od_index

[1] 118 132 119 110 106

Outlier Detection-ları vizuallaşdırmaq:

Outlier Detection-ların indeksləri:

df[distance, "Sepal.Length"]

  [1] 4.9 4.4 4.4 4.4 4.9 5.4 4.4 4.9 5.0 4.4 4.9 4.9 4.4 5.0 5.4 4.8 5.4
 [18] 4.9 5.4 4.9 4.9 4.9 4.4 4.9 4.9 4.4 4.9 4.9 4.9 4.4 4.4 4.9 4.9 5.4
 [35] 4.4 4.4 4.9 4.9 5.0 4.9 4.9 5.0 4.4 4.9 5.4 4.4 4.9 4.4 4.9 4.4 5.7
 [52] 5.8 5.7 4.8 5.8 4.3 5.8 5.4 5.8 4.8 5.4 4.3 4.8 5.8 4.8 5.8 4.3 4.8
 [69] 4.3 4.8 5.8 4.3 5.8 4.3 4.3 5.8 5.8 5.7 4.3 4.8 4.8 4.8 4.8 5.8 4.3
 [86] 5.8 5.7 4.3 4.3 4.8 4.8 5.8 4.8 5.4 4.8 4.3 4.3 4.3 5.4 4.8 5.1 5.8
[103] 5.1 5.7 5.4 5.7 4.8 5.1 5.7 5.7 5.7 5.7 5.4 5.8 5.7 5.4 5.7 5.1 5.7
[120] 4.3 5.1 5.8 5.7 5.8 5.4 5.1 5.8 5.8 5.7 5.4 5.1 5.1 5.4 5.8 5.8 5.7
[137] 5.4 5.7 5.8 5.4 5.4 5.4 5.8 5.1 5.1 5.4 5.8 5.7 5.4 5.8

df[distance, "Sepal.Width"]

  [1] 3.1 2.9 2.9 2.9 3.1 3.7 2.9 3.1 3.4 2.9 3.1 3.1 2.9 3.4 3.7 3.4 3.7
 [18] 3.1 3.7 3.1 3.1 3.1 2.9 3.1 3.1 2.9 3.1 3.1 3.1 2.9 2.9 3.1 3.1 3.7
 [35] 2.9 2.9 3.1 3.1 3.4 3.1 3.1 3.4 2.9 3.1 3.7 2.9 3.1 2.9 3.1 2.9 4.4
 [52] 4.0 4.4 3.0 4.0 3.0 4.0 3.7 4.0 3.0 3.7 3.0 3.0 4.0 3.0 4.0 3.0 3.0
 [69] 3.0 3.0 4.0 3.0 4.0 3.0 3.0 4.0 4.0 4.4 3.0 3.4 3.4 3.4 3.0 4.0 3.0
 [86] 4.0 4.4 3.0 3.0 3.0 3.0 4.0 3.0 3.7 3.0 3.0 3.0 3.0 3.7 3.0 3.5 4.0
[103] 3.5 4.4 3.9 3.8 3.0 3.5 4.4 3.8 4.4 4.4 3.9 4.0 4.4 3.9 4.4 3.8 3.8
[120] 3.0 3.5 4.0 3.8 4.0 3.9 3.5 4.0 4.0 4.4 3.9 3.5 3.8 3.9 4.0 4.0 3.8
[137] 3.9 4.4 4.0 3.9 3.9 3.9 4.0 3.5 3.5 3.9 4.0 4.4 3.9 4.0

df[distance, "Petal.Length"]

  [1] 1.5 1.4 1.4 1.4 1.5 1.5 1.4 1.5 1.5 1.4 1.5 1.5 1.4 1.5 1.5 1.6 1.5
 [18] 1.5 1.5 1.5 1.5 1.5 1.4 1.5 1.5 1.4 1.5 1.5 1.5 1.4 1.4 1.5 1.5 1.5
 [35] 1.4 1.4 1.5 1.5 1.5 1.5 1.5 1.5 1.4 1.5 1.5 1.4 1.5 1.4 1.5 1.4 1.5
 [52] 1.2 1.5 1.4 1.2 1.1 1.2 1.5 1.2 1.4 1.5 1.1 1.4 1.2 1.4 1.2 1.1 1.4
 [69] 1.1 1.4 1.2 1.1 1.2 1.1 1.1 1.2 1.2 1.5 1.1 1.6 1.6 1.6 1.4 1.2 1.1
 [86] 1.2 1.5 1.1 1.1 1.4 1.4 1.2 1.4 1.5 1.4 1.1 1.1 1.1 1.5 1.4 1.4 1.2
[103] 1.4 1.5 1.3 1.7 1.4 1.4 1.5 1.7 1.5 1.5 1.3 1.2 1.5 1.3 1.5 1.5 1.7
[120] 1.1 1.4 1.2 1.7 1.2 1.3 1.4 1.2 1.2 1.5 1.3 1.4 1.5 1.3 1.2 1.2 1.7
[137] 1.3 1.5 1.2 1.3 1.3 1.3 1.2 1.4 1.4 1.3 1.2 1.5 1.3 1.2

df[distance, "Petal.Width"]

  [1] 0.1 0.2 0.2 0.2 0.1 0.2 0.2 0.1 0.2 0.2 0.1 0.1 0.2 0.2 0.2 0.2 0.2
 [18] 0.1 0.2 0.1 0.1 0.1 0.2 0.1 0.1 0.2 0.1 0.1 0.1 0.2 0.2 0.1 0.1 0.2
 [35] 0.2 0.2 0.1 0.1 0.2 0.1 0.1 0.2 0.2 0.1 0.2 0.2 0.1 0.2 0.1 0.2 0.4
 [52] 0.2 0.4 0.1 0.2 0.1 0.2 0.2 0.2 0.1 0.2 0.1 0.1 0.2 0.1 0.2 0.1 0.1
 [69] 0.1 0.1 0.2 0.1 0.2 0.1 0.1 0.2 0.2 0.4 0.1 0.2 0.2 0.2 0.1 0.2 0.1
 [86] 0.2 0.4 0.1 0.1 0.1 0.1 0.2 0.1 0.2 0.1 0.1 0.1 0.1 0.2 0.1 0.3 0.2
[103] 0.3 0.4 0.4 0.3 0.1 0.3 0.4 0.3 0.4 0.4 0.4 0.2 0.4 0.4 0.4 0.3 0.3
[120] 0.1 0.3 0.2 0.3 0.2 0.4 0.3 0.2 0.2 0.4 0.4 0.3 0.3 0.4 0.2 0.2 0.3
[137] 0.4 0.4 0.2 0.4 0.4 0.4 0.2 0.3 0.3 0.4 0.2 0.4 0.4 0.2

Outlier Detectionları NA ilə əvəz etmək:

Outlier Detectionları təxmin etmə:

library(missForest)
rf_data <- missForest(df)

  missForest iteration 1 in progress...done!
  missForest iteration 2 in progress...done!
  missForest iteration 3 in progress...done!
  missForest iteration 4 in progress...done!

rf_data <- rf_data$ximp
rf_data

rf_data[distance,]

Yeni data üzərində model quraq:

2 mərkəzli klaster qurmaq:

library(DMwR)
k_means <- kmeans(rf_data, centers = 2)
k_means

K-means clustering with 2 clusters of sizes 40, 110

Cluster means:
  Sepal.Length Sepal.Width Petal.Length Petal.Width
1     4.972500    3.317500     1.602500    0.310000
2     6.239632    2.854059     4.852317    1.627464

Clustering vector:
  [1] 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 [36] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2
 [71] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 1 2 2 2 2 2 2
[106] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[141] 2 2 2 2 2 2 2 2 2 2

Within cluster sum of squares by cluster:
[1]  21.92325 140.90044
 (between_SS / total_SS =  71.8 %)

Available components:

[1] "cluster"      "centers"      "totss"        "withinss"    
[5] "tot.withinss" "betweenss"    "size"         "iter"        
[9] "ifault"      

Dəyişənlərin mərkəzləri ilə birlikdə ifadə edilməsi:

center_df <- k_means$centers[k_means$cluster,]
center_df

  Sepal.Length Sepal.Width Petal.Length Petal.Width
1     4.972500    3.317500     1.602500    0.310000
1     4.972500    3.317500     1.602500    0.310000
1     4.972500    3.317500     1.602500    0.310000
1     4.972500    3.317500     1.602500    0.310000
1     4.972500    3.317500     1.602500    0.310000
1     4.972500    3.317500     1.602500    0.310000
1     4.972500    3.317500     1.602500    0.310000
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
1     4.972500    3.317500     1.602500    0.310000
1     4.972500    3.317500     1.602500    0.310000
1     4.972500    3.317500     1.602500    0.310000
1     4.972500    3.317500     1.602500    0.310000
1     4.972500    3.317500     1.602500    0.310000
1     4.972500    3.317500     1.602500    0.310000
1     4.972500    3.317500     1.602500    0.310000
1     4.972500    3.317500     1.602500    0.310000
1     4.972500    3.317500     1.602500    0.310000
1     4.972500    3.317500     1.602500    0.310000
1     4.972500    3.317500     1.602500    0.310000
1     4.972500    3.317500     1.602500    0.310000
1     4.972500    3.317500     1.602500    0.310000
1     4.972500    3.317500     1.602500    0.310000
1     4.972500    3.317500     1.602500    0.310000
1     4.972500    3.317500     1.602500    0.310000
1     4.972500    3.317500     1.602500    0.310000
1     4.972500    3.317500     1.602500    0.310000
1     4.972500    3.317500     1.602500    0.310000
1     4.972500    3.317500     1.602500    0.310000
1     4.972500    3.317500     1.602500    0.310000
1     4.972500    3.317500     1.602500    0.310000
1     4.972500    3.317500     1.602500    0.310000
1     4.972500    3.317500     1.602500    0.310000
1     4.972500    3.317500     1.602500    0.310000
1     4.972500    3.317500     1.602500    0.310000
1     4.972500    3.317500     1.602500    0.310000
1     4.972500    3.317500     1.602500    0.310000
1     4.972500    3.317500     1.602500    0.310000
1     4.972500    3.317500     1.602500    0.310000
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
1     4.972500    3.317500     1.602500    0.310000
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
1     4.972500    3.317500     1.602500    0.310000
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
1     4.972500    3.317500     1.602500    0.310000
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464
2     6.239632    2.854059     4.852317    1.627464

Hər bir deyişən üçün uzaqlıqların hesablanması:

distance <- sqrt(rowSums(rf_data, center_df)^2)
distance

  [1] 10.20000  9.50000  9.40000  9.40000 10.20000 11.40000  9.70000
  [8] 13.56783 13.56783 13.56783 13.56783 13.56783 13.56783 13.56783
 [15] 13.56783 13.56783 13.56783 13.56783 13.56783 13.56783 10.70000
 [22] 10.70000  9.40000 10.60000 10.30000  9.80000 10.40000 10.40000
 [29] 10.20000  9.70000  9.70000 10.70000 10.90000 11.30000  9.70000
 [36]  9.60000 10.50000 10.00000  8.90000 10.20000 10.10000  8.40000
 [43]  9.10000 10.70000 11.20000  9.50000 10.70000  9.40000 10.70000
 [50]  9.90000 16.30000 15.60000 16.40000 13.10000 15.40000 14.30000
 [57] 15.90000 11.60000 15.40000 13.20000 11.50000 14.60000 13.20000
 [64] 15.10000 13.40000 15.60000 14.60000 13.60000 14.40000 13.10000
 [71] 15.70000 14.20000 15.20000 14.80000 14.90000 15.40000 15.80000
 [78] 16.40000 14.90000 12.80000 12.80000 12.60000 13.60000 15.40000
 [85] 14.40000 15.50000 16.00000 14.30000 14.00000 13.30000 13.70000
 [92] 15.10000 13.60000 11.60000 13.80000 14.10000 14.10000 14.70000
 [99] 11.70000 13.90000 18.10000 15.50000 18.10000 16.60000 17.50000
[106] 19.30000 13.60000 18.30000 16.80000 19.40000 16.80000 16.30000
[113] 17.40000 15.20000 16.10000 17.20000 16.80000 20.40000 19.50000
[120] 14.70000 18.10000 15.30000 19.20000 15.70000 17.80000 18.20000
[127] 15.60000 15.80000 16.90000 17.60000 18.20000 20.10000 17.00000
[134] 15.70000 15.70000 19.10000 17.70000 16.80000 15.60000 17.50000
[141] 17.80000 17.40000 15.50000 18.20000 18.20000 17.20000 15.70000
[148] 16.70000 17.30000 15.80000

Outlier Detection-ları vizuallaşdırmaq:

Nəticə:

Real datada olduğu kimi Random Forestlə təxmin etdiyimiz datanın tərkibində də outlier detectionlara rast gəlinir. Bunun səbəbi datadakı outlier detectionlar istər təxmin edildikdən, istər NA ilə doldurulduqdan, istərsə də ortalama dəyər ilə doldurulduqdan sonra yeni variyasalardan asılı olduğu üçün dəyişənlər müxtəlif şəkildə paylanır. Nəticə də isə yeni outlier detectionlar əmələ gəlir.