missing_val: Missing value imputation
In scorecardModelUtils: Credit Scorecard Modelling Utils
Description
Usage
Arguments
Value
Author(s)
Examples
Description
The function imputes the missing value in the input dataset. For numerical variables, missing values can be replaced by four possible method - 1. "mean" - mean or simple average of the non-missing values ; 2. - "median" - median or the 50th percentile of the non-missing values; 3. "mode"- mode or the value with maximum frequency among the non-mising values; 4. special extreme value of users' choice to be passes as an argument (-99999 is the default value). For categorical value, missing class can be replaced by two possible methods - 1. "mode" - mode or the class with maximum frequency among the non-mising values; 2. special class of users' choice to be passes as an argument ("missing_value" is the default class). The target column will remain unchanged.
Usage
1
2
missing_val(base, target, num_missing = -99999,
cat_missing = "missing_value")
Arguments
base
input dataframe
target
column/field name of the target variable, to be passed as a string
num_missing
(optional) method for replacing missing values for numerical type fields - to be chosen between "mean", "median", "mode" or a value of users' choice (default value is -99999)
cat_missing
(optional) method for replacing missing values for categorical type fields - to be chosen between "mode" or a class of users' choice (default value is "missing_value")
Value
The function returns an object of class "missing_val" which is a list containing the following components:
base
a dataframe after imputing missing values
mapping_table
a dataframe with mapping between original variable and imputed missing value (if any)
Author(s)
Arya Poddar <aryapoddar290990@gmail.com>
Examples
1
2
3
4
5
6
7
8
9
data <- iris
data$Species <- as.character(data$Species)
data$Y <- sample(0:1,size=nrow(data),replace=TRUE)
data[sample(1:nrow(data),size=25),"Sepal.Length"] <- NA
data[sample(1:nrow(data),size=10),"Species"] <- NA
missing_list <- missing_val(base = data,target = "Y")
missing_list$base
missing_list$mapping_table
Example output
Sepal.Length Sepal.Width Petal.Length Petal.Width Species Y
1 -99999.0 3.5 1.4 0.2 setosa 1
2 4.9 3.0 1.4 0.2 setosa 1
3 -99999.0 3.2 1.3 0.2 setosa 0
4 4.6 3.1 1.5 0.2 setosa 0
5 5.0 3.6 1.4 0.2 missing_value 1
6 -99999.0 3.9 1.7 0.4 setosa 0
7 4.6 3.4 1.4 0.3 missing_value 0
8 -99999.0 3.4 1.5 0.2 setosa 1
9 4.4 2.9 1.4 0.2 setosa 0
10 4.9 3.1 1.5 0.1 setosa 1
11 5.4 3.7 1.5 0.2 setosa 0
12 4.8 3.4 1.6 0.2 setosa 0
13 -99999.0 3.0 1.4 0.1 setosa 0
14 4.3 3.0 1.1 0.1 setosa 0
15 5.8 4.0 1.2 0.2 setosa 1
16 5.7 4.4 1.5 0.4 setosa 1
17 5.4 3.9 1.3 0.4 setosa 0
18 5.1 3.5 1.4 0.3 setosa 0
19 5.7 3.8 1.7 0.3 setosa 0
20 5.1 3.8 1.5 0.3 setosa 0
21 5.4 3.4 1.7 0.2 setosa 1
22 -99999.0 3.7 1.5 0.4 setosa 0
23 -99999.0 3.6 1.0 0.2 setosa 0
24 5.1 3.3 1.7 0.5 setosa 1
25 -99999.0 3.4 1.9 0.2 setosa 1
26 -99999.0 3.0 1.6 0.2 setosa 0
27 5.0 3.4 1.6 0.4 setosa 0
28 5.2 3.5 1.5 0.2 missing_value 1
29 5.2 3.4 1.4 0.2 setosa 0
30 4.7 3.2 1.6 0.2 setosa 1
31 4.8 3.1 1.6 0.2 setosa 1
32 5.4 3.4 1.5 0.4 setosa 0
33 5.2 4.1 1.5 0.1 setosa 0
34 5.5 4.2 1.4 0.2 setosa 0
35 4.9 3.1 1.5 0.2 setosa 0
36 5.0 3.2 1.2 0.2 setosa 0
37 5.5 3.5 1.3 0.2 missing_value 0
38 4.9 3.6 1.4 0.1 setosa 0
39 4.4 3.0 1.3 0.2 setosa 1
40 5.1 3.4 1.5 0.2 setosa 0
41 -99999.0 3.5 1.3 0.3 setosa 0
42 4.5 2.3 1.3 0.3 setosa 0
43 4.4 3.2 1.3 0.2 setosa 0
44 5.0 3.5 1.6 0.6 setosa 1
45 -99999.0 3.8 1.9 0.4 setosa 1
46 4.8 3.0 1.4 0.3 setosa 0
47 -99999.0 3.8 1.6 0.2 missing_value 1
48 4.6 3.2 1.4 0.2 setosa 0
49 5.3 3.7 1.5 0.2 setosa 1
50 5.0 3.3 1.4 0.2 setosa 0
51 7.0 3.2 4.7 1.4 versicolor 1
52 6.4 3.2 4.5 1.5 versicolor 1
53 6.9 3.1 4.9 1.5 versicolor 1
54 -99999.0 2.3 4.0 1.3 versicolor 0
55 6.5 2.8 4.6 1.5 versicolor 1
56 5.7 2.8 4.5 1.3 versicolor 1
57 6.3 3.3 4.7 1.6 missing_value 0
58 4.9 2.4 3.3 1.0 versicolor 1
59 6.6 2.9 4.6 1.3 versicolor 0
60 5.2 2.7 3.9 1.4 versicolor 0
61 5.0 2.0 3.5 1.0 versicolor 1
62 5.9 3.0 4.2 1.5 versicolor 0
63 -99999.0 2.2 4.0 1.0 versicolor 1
64 6.1 2.9 4.7 1.4 versicolor 0
65 5.6 2.9 3.6 1.3 versicolor 0
66 6.7 3.1 4.4 1.4 missing_value 1
67 5.6 3.0 4.5 1.5 missing_value 1
68 -99999.0 2.7 4.1 1.0 versicolor 1
69 6.2 2.2 4.5 1.5 versicolor 1
70 5.6 2.5 3.9 1.1 versicolor 1
71 5.9 3.2 4.8 1.8 versicolor 0
72 6.1 2.8 4.0 1.3 versicolor 1
73 6.3 2.5 4.9 1.5 versicolor 0
74 6.1 2.8 4.7 1.2 versicolor 0
75 -99999.0 2.9 4.3 1.3 versicolor 0
76 6.6 3.0 4.4 1.4 versicolor 1
77 6.8 2.8 4.8 1.4 versicolor 1
78 6.7 3.0 5.0 1.7 versicolor 0
79 6.0 2.9 4.5 1.5 versicolor 1
80 5.7 2.6 3.5 1.0 versicolor 1
81 5.5 2.4 3.8 1.1 versicolor 1
82 5.5 2.4 3.7 1.0 versicolor 1
83 5.8 2.7 3.9 1.2 versicolor 1
84 6.0 2.7 5.1 1.6 versicolor 1
85 5.4 3.0 4.5 1.5 versicolor 0
86 6.0 3.4 4.5 1.6 versicolor 0
87 6.7 3.1 4.7 1.5 versicolor 0
88 6.3 2.3 4.4 1.3 versicolor 0
89 5.6 3.0 4.1 1.3 versicolor 1
90 5.5 2.5 4.0 1.3 versicolor 0
91 5.5 2.6 4.4 1.2 versicolor 0
92 6.1 3.0 4.6 1.4 versicolor 1
93 -99999.0 2.6 4.0 1.2 versicolor 0
94 5.0 2.3 3.3 1.0 versicolor 0
95 5.6 2.7 4.2 1.3 versicolor 1
96 5.7 3.0 4.2 1.2 versicolor 0
97 5.7 2.9 4.2 1.3 versicolor 1
98 6.2 2.9 4.3 1.3 versicolor 0
99 -99999.0 2.5 3.0 1.1 versicolor 1
100 5.7 2.8 4.1 1.3 versicolor 1
101 -99999.0 3.3 6.0 2.5 virginica 1
102 5.8 2.7 5.1 1.9 virginica 0
103 7.1 3.0 5.9 2.1 virginica 0
104 6.3 2.9 5.6 1.8 virginica 0
105 6.5 3.0 5.8 2.2 virginica 1
106 7.6 3.0 6.6 2.1 virginica 0
107 4.9 2.5 4.5 1.7 virginica 1
108 -99999.0 2.9 6.3 1.8 virginica 1
109 6.7 2.5 5.8 1.8 virginica 1
110 -99999.0 3.6 6.1 2.5 virginica 1
111 6.5 3.2 5.1 2.0 virginica 1
112 6.4 2.7 5.3 1.9 virginica 1
113 6.8 3.0 5.5 2.1 virginica 0
114 5.7 2.5 5.0 2.0 virginica 0
115 5.8 2.8 5.1 2.4 virginica 0
116 6.4 3.2 5.3 2.3 virginica 0
117 6.5 3.0 5.5 1.8 virginica 0
118 7.7 3.8 6.7 2.2 virginica 0
119 7.7 2.6 6.9 2.3 virginica 1
120 6.0 2.2 5.0 1.5 virginica 0
121 6.9 3.2 5.7 2.3 virginica 0
122 -99999.0 2.8 4.9 2.0 virginica 1
123 7.7 2.8 6.7 2.0 virginica 0
124 6.3 2.7 4.9 1.8 virginica 0
125 6.7 3.3 5.7 2.1 virginica 0
126 7.2 3.2 6.0 1.8 virginica 0
127 6.2 2.8 4.8 1.8 virginica 1
128 6.1 3.0 4.9 1.8 virginica 1
129 6.4 2.8 5.6 2.1 virginica 0
130 7.2 3.0 5.8 1.6 virginica 0
131 -99999.0 2.8 6.1 1.9 virginica 1
132 7.9 3.8 6.4 2.0 virginica 1
133 6.4 2.8 5.6 2.2 virginica 0
134 -99999.0 2.8 5.1 1.5 virginica 0
135 6.1 2.6 5.6 1.4 virginica 0
136 -99999.0 3.0 6.1 2.3 virginica 1
137 6.3 3.4 5.6 2.4 virginica 0
138 6.4 3.1 5.5 1.8 virginica 0
139 6.0 3.0 4.8 1.8 virginica 0
140 6.9 3.1 5.4 2.1 virginica 0
141 6.7 3.1 5.6 2.4 virginica 0
142 6.9 3.1 5.1 2.3 virginica 0
143 5.8 2.7 5.1 1.9 virginica 1
144 6.8 3.2 5.9 2.3 virginica 0
145 6.7 3.3 5.7 2.5 missing_value 0
146 6.7 3.0 5.2 2.3 virginica 0
147 6.3 2.5 5.0 1.9 virginica 0
148 6.5 3.0 5.2 2.0 virginica 1
149 6.2 3.4 5.4 2.3 missing_value 1
150 5.9 3.0 5.1 1.8 virginica 1
Variable_name imputed_missing
1 Sepal.Length -99999
2 Sepal.Width -99999
3 Petal.Length -99999
4 Petal.Width -99999
5 Species missing_value
scorecardModelUtils documentation built on May 2, 2019, 9:59 a.m.
R Package Documentation
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Description Usage Arguments Value Author(s) Examples
Description
The function imputes the missing value in the input dataset. For numerical variables, missing values can be replaced by four possible method - 1. "mean" - mean or simple average of the non-missing values ; 2. - "median" - median or the 50th percentile of the non-missing values; 3. "mode"- mode or the value with maximum frequency among the non-mising values; 4. special extreme value of users' choice to be passes as an argument (-99999 is the default value). For categorical value, missing class can be replaced by two possible methods - 1. "mode" - mode or the class with maximum frequency among the non-mising values; 2. special class of users' choice to be passes as an argument ("missing_value" is the default class). The target column will remain unchanged.
Usage
1 2 | missing_val(base, target, num_missing = -99999,
cat_missing = "missing_value")
|
Arguments
base |
input dataframe |
target |
column/field name of the target variable, to be passed as a string |
num_missing |
(optional) method for replacing missing values for numerical type fields - to be chosen between "mean", "median", "mode" or a value of users' choice (default value is -99999) |
cat_missing |
(optional) method for replacing missing values for categorical type fields - to be chosen between "mode" or a class of users' choice (default value is "missing_value") |
Value
The function returns an object of class "missing_val" which is a list containing the following components:
base |
a dataframe after imputing missing values |
mapping_table |
a dataframe with mapping between original variable and imputed missing value (if any) |
Author(s)
Arya Poddar <aryapoddar290990@gmail.com>
Examples
1 2 3 4 5 6 7 8 9 | data <- iris
data$Species <- as.character(data$Species)
data$Y <- sample(0:1,size=nrow(data),replace=TRUE)
data[sample(1:nrow(data),size=25),"Sepal.Length"] <- NA
data[sample(1:nrow(data),size=10),"Species"] <- NA
missing_list <- missing_val(base = data,target = "Y")
missing_list$base
missing_list$mapping_table
|
Example output
Sepal.Length Sepal.Width Petal.Length Petal.Width Species Y
1 -99999.0 3.5 1.4 0.2 setosa 1
2 4.9 3.0 1.4 0.2 setosa 1
3 -99999.0 3.2 1.3 0.2 setosa 0
4 4.6 3.1 1.5 0.2 setosa 0
5 5.0 3.6 1.4 0.2 missing_value 1
6 -99999.0 3.9 1.7 0.4 setosa 0
7 4.6 3.4 1.4 0.3 missing_value 0
8 -99999.0 3.4 1.5 0.2 setosa 1
9 4.4 2.9 1.4 0.2 setosa 0
10 4.9 3.1 1.5 0.1 setosa 1
11 5.4 3.7 1.5 0.2 setosa 0
12 4.8 3.4 1.6 0.2 setosa 0
13 -99999.0 3.0 1.4 0.1 setosa 0
14 4.3 3.0 1.1 0.1 setosa 0
15 5.8 4.0 1.2 0.2 setosa 1
16 5.7 4.4 1.5 0.4 setosa 1
17 5.4 3.9 1.3 0.4 setosa 0
18 5.1 3.5 1.4 0.3 setosa 0
19 5.7 3.8 1.7 0.3 setosa 0
20 5.1 3.8 1.5 0.3 setosa 0
21 5.4 3.4 1.7 0.2 setosa 1
22 -99999.0 3.7 1.5 0.4 setosa 0
23 -99999.0 3.6 1.0 0.2 setosa 0
24 5.1 3.3 1.7 0.5 setosa 1
25 -99999.0 3.4 1.9 0.2 setosa 1
26 -99999.0 3.0 1.6 0.2 setosa 0
27 5.0 3.4 1.6 0.4 setosa 0
28 5.2 3.5 1.5 0.2 missing_value 1
29 5.2 3.4 1.4 0.2 setosa 0
30 4.7 3.2 1.6 0.2 setosa 1
31 4.8 3.1 1.6 0.2 setosa 1
32 5.4 3.4 1.5 0.4 setosa 0
33 5.2 4.1 1.5 0.1 setosa 0
34 5.5 4.2 1.4 0.2 setosa 0
35 4.9 3.1 1.5 0.2 setosa 0
36 5.0 3.2 1.2 0.2 setosa 0
37 5.5 3.5 1.3 0.2 missing_value 0
38 4.9 3.6 1.4 0.1 setosa 0
39 4.4 3.0 1.3 0.2 setosa 1
40 5.1 3.4 1.5 0.2 setosa 0
41 -99999.0 3.5 1.3 0.3 setosa 0
42 4.5 2.3 1.3 0.3 setosa 0
43 4.4 3.2 1.3 0.2 setosa 0
44 5.0 3.5 1.6 0.6 setosa 1
45 -99999.0 3.8 1.9 0.4 setosa 1
46 4.8 3.0 1.4 0.3 setosa 0
47 -99999.0 3.8 1.6 0.2 missing_value 1
48 4.6 3.2 1.4 0.2 setosa 0
49 5.3 3.7 1.5 0.2 setosa 1
50 5.0 3.3 1.4 0.2 setosa 0
51 7.0 3.2 4.7 1.4 versicolor 1
52 6.4 3.2 4.5 1.5 versicolor 1
53 6.9 3.1 4.9 1.5 versicolor 1
54 -99999.0 2.3 4.0 1.3 versicolor 0
55 6.5 2.8 4.6 1.5 versicolor 1
56 5.7 2.8 4.5 1.3 versicolor 1
57 6.3 3.3 4.7 1.6 missing_value 0
58 4.9 2.4 3.3 1.0 versicolor 1
59 6.6 2.9 4.6 1.3 versicolor 0
60 5.2 2.7 3.9 1.4 versicolor 0
61 5.0 2.0 3.5 1.0 versicolor 1
62 5.9 3.0 4.2 1.5 versicolor 0
63 -99999.0 2.2 4.0 1.0 versicolor 1
64 6.1 2.9 4.7 1.4 versicolor 0
65 5.6 2.9 3.6 1.3 versicolor 0
66 6.7 3.1 4.4 1.4 missing_value 1
67 5.6 3.0 4.5 1.5 missing_value 1
68 -99999.0 2.7 4.1 1.0 versicolor 1
69 6.2 2.2 4.5 1.5 versicolor 1
70 5.6 2.5 3.9 1.1 versicolor 1
71 5.9 3.2 4.8 1.8 versicolor 0
72 6.1 2.8 4.0 1.3 versicolor 1
73 6.3 2.5 4.9 1.5 versicolor 0
74 6.1 2.8 4.7 1.2 versicolor 0
75 -99999.0 2.9 4.3 1.3 versicolor 0
76 6.6 3.0 4.4 1.4 versicolor 1
77 6.8 2.8 4.8 1.4 versicolor 1
78 6.7 3.0 5.0 1.7 versicolor 0
79 6.0 2.9 4.5 1.5 versicolor 1
80 5.7 2.6 3.5 1.0 versicolor 1
81 5.5 2.4 3.8 1.1 versicolor 1
82 5.5 2.4 3.7 1.0 versicolor 1
83 5.8 2.7 3.9 1.2 versicolor 1
84 6.0 2.7 5.1 1.6 versicolor 1
85 5.4 3.0 4.5 1.5 versicolor 0
86 6.0 3.4 4.5 1.6 versicolor 0
87 6.7 3.1 4.7 1.5 versicolor 0
88 6.3 2.3 4.4 1.3 versicolor 0
89 5.6 3.0 4.1 1.3 versicolor 1
90 5.5 2.5 4.0 1.3 versicolor 0
91 5.5 2.6 4.4 1.2 versicolor 0
92 6.1 3.0 4.6 1.4 versicolor 1
93 -99999.0 2.6 4.0 1.2 versicolor 0
94 5.0 2.3 3.3 1.0 versicolor 0
95 5.6 2.7 4.2 1.3 versicolor 1
96 5.7 3.0 4.2 1.2 versicolor 0
97 5.7 2.9 4.2 1.3 versicolor 1
98 6.2 2.9 4.3 1.3 versicolor 0
99 -99999.0 2.5 3.0 1.1 versicolor 1
100 5.7 2.8 4.1 1.3 versicolor 1
101 -99999.0 3.3 6.0 2.5 virginica 1
102 5.8 2.7 5.1 1.9 virginica 0
103 7.1 3.0 5.9 2.1 virginica 0
104 6.3 2.9 5.6 1.8 virginica 0
105 6.5 3.0 5.8 2.2 virginica 1
106 7.6 3.0 6.6 2.1 virginica 0
107 4.9 2.5 4.5 1.7 virginica 1
108 -99999.0 2.9 6.3 1.8 virginica 1
109 6.7 2.5 5.8 1.8 virginica 1
110 -99999.0 3.6 6.1 2.5 virginica 1
111 6.5 3.2 5.1 2.0 virginica 1
112 6.4 2.7 5.3 1.9 virginica 1
113 6.8 3.0 5.5 2.1 virginica 0
114 5.7 2.5 5.0 2.0 virginica 0
115 5.8 2.8 5.1 2.4 virginica 0
116 6.4 3.2 5.3 2.3 virginica 0
117 6.5 3.0 5.5 1.8 virginica 0
118 7.7 3.8 6.7 2.2 virginica 0
119 7.7 2.6 6.9 2.3 virginica 1
120 6.0 2.2 5.0 1.5 virginica 0
121 6.9 3.2 5.7 2.3 virginica 0
122 -99999.0 2.8 4.9 2.0 virginica 1
123 7.7 2.8 6.7 2.0 virginica 0
124 6.3 2.7 4.9 1.8 virginica 0
125 6.7 3.3 5.7 2.1 virginica 0
126 7.2 3.2 6.0 1.8 virginica 0
127 6.2 2.8 4.8 1.8 virginica 1
128 6.1 3.0 4.9 1.8 virginica 1
129 6.4 2.8 5.6 2.1 virginica 0
130 7.2 3.0 5.8 1.6 virginica 0
131 -99999.0 2.8 6.1 1.9 virginica 1
132 7.9 3.8 6.4 2.0 virginica 1
133 6.4 2.8 5.6 2.2 virginica 0
134 -99999.0 2.8 5.1 1.5 virginica 0
135 6.1 2.6 5.6 1.4 virginica 0
136 -99999.0 3.0 6.1 2.3 virginica 1
137 6.3 3.4 5.6 2.4 virginica 0
138 6.4 3.1 5.5 1.8 virginica 0
139 6.0 3.0 4.8 1.8 virginica 0
140 6.9 3.1 5.4 2.1 virginica 0
141 6.7 3.1 5.6 2.4 virginica 0
142 6.9 3.1 5.1 2.3 virginica 0
143 5.8 2.7 5.1 1.9 virginica 1
144 6.8 3.2 5.9 2.3 virginica 0
145 6.7 3.3 5.7 2.5 missing_value 0
146 6.7 3.0 5.2 2.3 virginica 0
147 6.3 2.5 5.0 1.9 virginica 0
148 6.5 3.0 5.2 2.0 virginica 1
149 6.2 3.4 5.4 2.3 missing_value 1
150 5.9 3.0 5.1 1.8 virginica 1
Variable_name imputed_missing
1 Sepal.Length -99999
2 Sepal.Width -99999
3 Petal.Length -99999
4 Petal.Width -99999
5 Species missing_value
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