catto_loo: Leave-one-out encoding
In cattonum: Encode Categorical Features
Description
Usage
Arguments
Value
Examples
Description
Leave-one-out encoding
Usage
1
Arguments
train
The training data, in a data.frame or tibble.
...
The columns to be encoded. If none are specified, then
all character and factor columns are encoded.
response
The response variable used to calculate means.
test
The test data, in a data.frame or tibble.
verbose
Should informative messages be printed? Defaults to
TRUE.
Value
The encoded dataset in a cattonum_df if no test dataset was
provided, and the encoded datasets in a cattonum_df2 otherwise.
Examples
1
Example output
cattonum is seeking a new maintainer; please respond if interested: https://github.com/bfgray3/cattonum/issues/40
# A cattonum_df with the following data:
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3.5 1.4 0.2 5.004082
2 4.9 3.0 1.4 0.2 5.008163
3 4.7 3.2 1.3 0.2 5.012245
4 4.6 3.1 1.5 0.2 5.014286
5 5.0 3.6 1.4 0.2 5.006122
6 5.4 3.9 1.7 0.4 4.997959
7 4.6 3.4 1.4 0.3 5.014286
8 5.0 3.4 1.5 0.2 5.006122
9 4.4 2.9 1.4 0.2 5.018367
10 4.9 3.1 1.5 0.1 5.008163
11 5.4 3.7 1.5 0.2 4.997959
12 4.8 3.4 1.6 0.2 5.010204
13 4.8 3.0 1.4 0.1 5.010204
14 4.3 3.0 1.1 0.1 5.020408
15 5.8 4.0 1.2 0.2 4.989796
16 5.7 4.4 1.5 0.4 4.991837
17 5.4 3.9 1.3 0.4 4.997959
18 5.1 3.5 1.4 0.3 5.004082
19 5.7 3.8 1.7 0.3 4.991837
20 5.1 3.8 1.5 0.3 5.004082
21 5.4 3.4 1.7 0.2 4.997959
22 5.1 3.7 1.5 0.4 5.004082
23 4.6 3.6 1.0 0.2 5.014286
24 5.1 3.3 1.7 0.5 5.004082
25 4.8 3.4 1.9 0.2 5.010204
26 5.0 3.0 1.6 0.2 5.006122
27 5.0 3.4 1.6 0.4 5.006122
28 5.2 3.5 1.5 0.2 5.002041
29 5.2 3.4 1.4 0.2 5.002041
30 4.7 3.2 1.6 0.2 5.012245
31 4.8 3.1 1.6 0.2 5.010204
32 5.4 3.4 1.5 0.4 4.997959
33 5.2 4.1 1.5 0.1 5.002041
34 5.5 4.2 1.4 0.2 4.995918
35 4.9 3.1 1.5 0.2 5.008163
36 5.0 3.2 1.2 0.2 5.006122
37 5.5 3.5 1.3 0.2 4.995918
38 4.9 3.6 1.4 0.1 5.008163
39 4.4 3.0 1.3 0.2 5.018367
40 5.1 3.4 1.5 0.2 5.004082
41 5.0 3.5 1.3 0.3 5.006122
42 4.5 2.3 1.3 0.3 5.016327
43 4.4 3.2 1.3 0.2 5.018367
44 5.0 3.5 1.6 0.6 5.006122
45 5.1 3.8 1.9 0.4 5.004082
46 4.8 3.0 1.4 0.3 5.010204
47 5.1 3.8 1.6 0.2 5.004082
48 4.6 3.2 1.4 0.2 5.014286
49 5.3 3.7 1.5 0.2 5.000000
50 5.0 3.3 1.4 0.2 5.006122
51 7.0 3.2 4.7 1.4 5.914286
52 6.4 3.2 4.5 1.5 5.926531
53 6.9 3.1 4.9 1.5 5.916327
54 5.5 2.3 4.0 1.3 5.944898
55 6.5 2.8 4.6 1.5 5.924490
56 5.7 2.8 4.5 1.3 5.940816
57 6.3 3.3 4.7 1.6 5.928571
58 4.9 2.4 3.3 1.0 5.957143
59 6.6 2.9 4.6 1.3 5.922449
60 5.2 2.7 3.9 1.4 5.951020
61 5.0 2.0 3.5 1.0 5.955102
62 5.9 3.0 4.2 1.5 5.936735
63 6.0 2.2 4.0 1.0 5.934694
64 6.1 2.9 4.7 1.4 5.932653
65 5.6 2.9 3.6 1.3 5.942857
66 6.7 3.1 4.4 1.4 5.920408
67 5.6 3.0 4.5 1.5 5.942857
68 5.8 2.7 4.1 1.0 5.938776
69 6.2 2.2 4.5 1.5 5.930612
70 5.6 2.5 3.9 1.1 5.942857
71 5.9 3.2 4.8 1.8 5.936735
72 6.1 2.8 4.0 1.3 5.932653
73 6.3 2.5 4.9 1.5 5.928571
74 6.1 2.8 4.7 1.2 5.932653
75 6.4 2.9 4.3 1.3 5.926531
76 6.6 3.0 4.4 1.4 5.922449
77 6.8 2.8 4.8 1.4 5.918367
78 6.7 3.0 5.0 1.7 5.920408
79 6.0 2.9 4.5 1.5 5.934694
80 5.7 2.6 3.5 1.0 5.940816
81 5.5 2.4 3.8 1.1 5.944898
82 5.5 2.4 3.7 1.0 5.944898
83 5.8 2.7 3.9 1.2 5.938776
84 6.0 2.7 5.1 1.6 5.934694
85 5.4 3.0 4.5 1.5 5.946939
86 6.0 3.4 4.5 1.6 5.934694
87 6.7 3.1 4.7 1.5 5.920408
88 6.3 2.3 4.4 1.3 5.928571
89 5.6 3.0 4.1 1.3 5.942857
90 5.5 2.5 4.0 1.3 5.944898
91 5.5 2.6 4.4 1.2 5.944898
92 6.1 3.0 4.6 1.4 5.932653
93 5.8 2.6 4.0 1.2 5.938776
94 5.0 2.3 3.3 1.0 5.955102
95 5.6 2.7 4.2 1.3 5.942857
96 5.7 3.0 4.2 1.2 5.940816
97 5.7 2.9 4.2 1.3 5.940816
98 6.2 2.9 4.3 1.3 5.930612
99 5.1 2.5 3.0 1.1 5.953061
100 5.7 2.8 4.1 1.3 5.940816
101 6.3 3.3 6.0 2.5 6.593878
102 5.8 2.7 5.1 1.9 6.604082
103 7.1 3.0 5.9 2.1 6.577551
104 6.3 2.9 5.6 1.8 6.593878
105 6.5 3.0 5.8 2.2 6.589796
106 7.6 3.0 6.6 2.1 6.567347
107 4.9 2.5 4.5 1.7 6.622449
108 7.3 2.9 6.3 1.8 6.573469
109 6.7 2.5 5.8 1.8 6.585714
110 7.2 3.6 6.1 2.5 6.575510
111 6.5 3.2 5.1 2.0 6.589796
112 6.4 2.7 5.3 1.9 6.591837
113 6.8 3.0 5.5 2.1 6.583673
114 5.7 2.5 5.0 2.0 6.606122
115 5.8 2.8 5.1 2.4 6.604082
116 6.4 3.2 5.3 2.3 6.591837
117 6.5 3.0 5.5 1.8 6.589796
118 7.7 3.8 6.7 2.2 6.565306
119 7.7 2.6 6.9 2.3 6.565306
120 6.0 2.2 5.0 1.5 6.600000
121 6.9 3.2 5.7 2.3 6.581633
122 5.6 2.8 4.9 2.0 6.608163
123 7.7 2.8 6.7 2.0 6.565306
124 6.3 2.7 4.9 1.8 6.593878
125 6.7 3.3 5.7 2.1 6.585714
126 7.2 3.2 6.0 1.8 6.575510
127 6.2 2.8 4.8 1.8 6.595918
128 6.1 3.0 4.9 1.8 6.597959
129 6.4 2.8 5.6 2.1 6.591837
130 7.2 3.0 5.8 1.6 6.575510
131 7.4 2.8 6.1 1.9 6.571429
132 7.9 3.8 6.4 2.0 6.561224
133 6.4 2.8 5.6 2.2 6.591837
134 6.3 2.8 5.1 1.5 6.593878
135 6.1 2.6 5.6 1.4 6.597959
136 7.7 3.0 6.1 2.3 6.565306
137 6.3 3.4 5.6 2.4 6.593878
138 6.4 3.1 5.5 1.8 6.591837
139 6.0 3.0 4.8 1.8 6.600000
140 6.9 3.1 5.4 2.1 6.581633
141 6.7 3.1 5.6 2.4 6.585714
142 6.9 3.1 5.1 2.3 6.581633
143 5.8 2.7 5.1 1.9 6.604082
144 6.8 3.2 5.9 2.3 6.583673
145 6.7 3.3 5.7 2.5 6.585714
146 6.7 3.0 5.2 2.3 6.585714
147 6.3 2.5 5.0 1.9 6.593878
148 6.5 3.0 5.2 2.0 6.589796
149 6.2 3.4 5.4 2.3 6.595918
150 5.9 3.0 5.1 1.8 6.602041
cattonum documentation built on July 2, 2020, 2:26 a.m.
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Description Usage Arguments Value Examples
Description
Leave-one-out encoding
Usage
1 |
Arguments
train |
The training data, in a |
... |
The columns to be encoded. If none are specified, then all character and factor columns are encoded. |
response |
The response variable used to calculate means. |
test |
The test data, in a |
verbose |
Should informative messages be printed? Defaults to
|
Value
The encoded dataset in a cattonum_df if no test dataset was
provided, and the encoded datasets in a cattonum_df2 otherwise.
Examples
1 |
Example output
cattonum is seeking a new maintainer; please respond if interested: https://github.com/bfgray3/cattonum/issues/40
# A cattonum_df with the following data:
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3.5 1.4 0.2 5.004082
2 4.9 3.0 1.4 0.2 5.008163
3 4.7 3.2 1.3 0.2 5.012245
4 4.6 3.1 1.5 0.2 5.014286
5 5.0 3.6 1.4 0.2 5.006122
6 5.4 3.9 1.7 0.4 4.997959
7 4.6 3.4 1.4 0.3 5.014286
8 5.0 3.4 1.5 0.2 5.006122
9 4.4 2.9 1.4 0.2 5.018367
10 4.9 3.1 1.5 0.1 5.008163
11 5.4 3.7 1.5 0.2 4.997959
12 4.8 3.4 1.6 0.2 5.010204
13 4.8 3.0 1.4 0.1 5.010204
14 4.3 3.0 1.1 0.1 5.020408
15 5.8 4.0 1.2 0.2 4.989796
16 5.7 4.4 1.5 0.4 4.991837
17 5.4 3.9 1.3 0.4 4.997959
18 5.1 3.5 1.4 0.3 5.004082
19 5.7 3.8 1.7 0.3 4.991837
20 5.1 3.8 1.5 0.3 5.004082
21 5.4 3.4 1.7 0.2 4.997959
22 5.1 3.7 1.5 0.4 5.004082
23 4.6 3.6 1.0 0.2 5.014286
24 5.1 3.3 1.7 0.5 5.004082
25 4.8 3.4 1.9 0.2 5.010204
26 5.0 3.0 1.6 0.2 5.006122
27 5.0 3.4 1.6 0.4 5.006122
28 5.2 3.5 1.5 0.2 5.002041
29 5.2 3.4 1.4 0.2 5.002041
30 4.7 3.2 1.6 0.2 5.012245
31 4.8 3.1 1.6 0.2 5.010204
32 5.4 3.4 1.5 0.4 4.997959
33 5.2 4.1 1.5 0.1 5.002041
34 5.5 4.2 1.4 0.2 4.995918
35 4.9 3.1 1.5 0.2 5.008163
36 5.0 3.2 1.2 0.2 5.006122
37 5.5 3.5 1.3 0.2 4.995918
38 4.9 3.6 1.4 0.1 5.008163
39 4.4 3.0 1.3 0.2 5.018367
40 5.1 3.4 1.5 0.2 5.004082
41 5.0 3.5 1.3 0.3 5.006122
42 4.5 2.3 1.3 0.3 5.016327
43 4.4 3.2 1.3 0.2 5.018367
44 5.0 3.5 1.6 0.6 5.006122
45 5.1 3.8 1.9 0.4 5.004082
46 4.8 3.0 1.4 0.3 5.010204
47 5.1 3.8 1.6 0.2 5.004082
48 4.6 3.2 1.4 0.2 5.014286
49 5.3 3.7 1.5 0.2 5.000000
50 5.0 3.3 1.4 0.2 5.006122
51 7.0 3.2 4.7 1.4 5.914286
52 6.4 3.2 4.5 1.5 5.926531
53 6.9 3.1 4.9 1.5 5.916327
54 5.5 2.3 4.0 1.3 5.944898
55 6.5 2.8 4.6 1.5 5.924490
56 5.7 2.8 4.5 1.3 5.940816
57 6.3 3.3 4.7 1.6 5.928571
58 4.9 2.4 3.3 1.0 5.957143
59 6.6 2.9 4.6 1.3 5.922449
60 5.2 2.7 3.9 1.4 5.951020
61 5.0 2.0 3.5 1.0 5.955102
62 5.9 3.0 4.2 1.5 5.936735
63 6.0 2.2 4.0 1.0 5.934694
64 6.1 2.9 4.7 1.4 5.932653
65 5.6 2.9 3.6 1.3 5.942857
66 6.7 3.1 4.4 1.4 5.920408
67 5.6 3.0 4.5 1.5 5.942857
68 5.8 2.7 4.1 1.0 5.938776
69 6.2 2.2 4.5 1.5 5.930612
70 5.6 2.5 3.9 1.1 5.942857
71 5.9 3.2 4.8 1.8 5.936735
72 6.1 2.8 4.0 1.3 5.932653
73 6.3 2.5 4.9 1.5 5.928571
74 6.1 2.8 4.7 1.2 5.932653
75 6.4 2.9 4.3 1.3 5.926531
76 6.6 3.0 4.4 1.4 5.922449
77 6.8 2.8 4.8 1.4 5.918367
78 6.7 3.0 5.0 1.7 5.920408
79 6.0 2.9 4.5 1.5 5.934694
80 5.7 2.6 3.5 1.0 5.940816
81 5.5 2.4 3.8 1.1 5.944898
82 5.5 2.4 3.7 1.0 5.944898
83 5.8 2.7 3.9 1.2 5.938776
84 6.0 2.7 5.1 1.6 5.934694
85 5.4 3.0 4.5 1.5 5.946939
86 6.0 3.4 4.5 1.6 5.934694
87 6.7 3.1 4.7 1.5 5.920408
88 6.3 2.3 4.4 1.3 5.928571
89 5.6 3.0 4.1 1.3 5.942857
90 5.5 2.5 4.0 1.3 5.944898
91 5.5 2.6 4.4 1.2 5.944898
92 6.1 3.0 4.6 1.4 5.932653
93 5.8 2.6 4.0 1.2 5.938776
94 5.0 2.3 3.3 1.0 5.955102
95 5.6 2.7 4.2 1.3 5.942857
96 5.7 3.0 4.2 1.2 5.940816
97 5.7 2.9 4.2 1.3 5.940816
98 6.2 2.9 4.3 1.3 5.930612
99 5.1 2.5 3.0 1.1 5.953061
100 5.7 2.8 4.1 1.3 5.940816
101 6.3 3.3 6.0 2.5 6.593878
102 5.8 2.7 5.1 1.9 6.604082
103 7.1 3.0 5.9 2.1 6.577551
104 6.3 2.9 5.6 1.8 6.593878
105 6.5 3.0 5.8 2.2 6.589796
106 7.6 3.0 6.6 2.1 6.567347
107 4.9 2.5 4.5 1.7 6.622449
108 7.3 2.9 6.3 1.8 6.573469
109 6.7 2.5 5.8 1.8 6.585714
110 7.2 3.6 6.1 2.5 6.575510
111 6.5 3.2 5.1 2.0 6.589796
112 6.4 2.7 5.3 1.9 6.591837
113 6.8 3.0 5.5 2.1 6.583673
114 5.7 2.5 5.0 2.0 6.606122
115 5.8 2.8 5.1 2.4 6.604082
116 6.4 3.2 5.3 2.3 6.591837
117 6.5 3.0 5.5 1.8 6.589796
118 7.7 3.8 6.7 2.2 6.565306
119 7.7 2.6 6.9 2.3 6.565306
120 6.0 2.2 5.0 1.5 6.600000
121 6.9 3.2 5.7 2.3 6.581633
122 5.6 2.8 4.9 2.0 6.608163
123 7.7 2.8 6.7 2.0 6.565306
124 6.3 2.7 4.9 1.8 6.593878
125 6.7 3.3 5.7 2.1 6.585714
126 7.2 3.2 6.0 1.8 6.575510
127 6.2 2.8 4.8 1.8 6.595918
128 6.1 3.0 4.9 1.8 6.597959
129 6.4 2.8 5.6 2.1 6.591837
130 7.2 3.0 5.8 1.6 6.575510
131 7.4 2.8 6.1 1.9 6.571429
132 7.9 3.8 6.4 2.0 6.561224
133 6.4 2.8 5.6 2.2 6.591837
134 6.3 2.8 5.1 1.5 6.593878
135 6.1 2.6 5.6 1.4 6.597959
136 7.7 3.0 6.1 2.3 6.565306
137 6.3 3.4 5.6 2.4 6.593878
138 6.4 3.1 5.5 1.8 6.591837
139 6.0 3.0 4.8 1.8 6.600000
140 6.9 3.1 5.4 2.1 6.581633
141 6.7 3.1 5.6 2.4 6.585714
142 6.9 3.1 5.1 2.3 6.581633
143 5.8 2.7 5.1 1.9 6.604082
144 6.8 3.2 5.9 2.3 6.583673
145 6.7 3.3 5.7 2.5 6.585714
146 6.7 3.0 5.2 2.3 6.585714
147 6.3 2.5 5.0 1.9 6.593878
148 6.5 3.0 5.2 2.0 6.589796
149 6.2 3.4 5.4 2.3 6.595918
150 5.9 3.0 5.1 1.8 6.602041
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