Description Usage Arguments Value Author(s) Examples
The function takes a dataframe along with a model or the name of a column with predicted value. If a model (only lm or glm works is guaranted to work perfectly) is provided as argument, the response on the data is predicted. Otherwise, if the data already contains a predicted column, it can be referred as an argument. The predicted column, thus obtained, is classified into bands to get the Gini coefficient, Kolmogorov-Smirnov statistics and Gini lift curve. The number of bands required can be passed as argument, with default value as 10 ie. decile binning is done. Otherwise, the cutpoints for converting the predicted value into bands can also be specified.
1 2 | gini_table(base, target, col_pred = F, model = F, brk = F,
quantile_pt = 10, event_rate_direction = "decreasing")
|
base |
input dataframe |
target |
column / field name for the target variable to be passed as string (must be 0/1 type) |
col_pred |
(optional) column name which contains the predicted value, not required if "model"=TRUE (default value is FALSE) |
model |
(optional) object of type lm or glm model, required only if "col_pred"=FALSE (default value is FALSE) |
brk |
(optional) array of break points of predicted value (default value is FALSE) |
quantile_pt |
(optional) number of quantiles to divide the predicted value range (default value is 10) |
event_rate_direction |
(optional) directionality of event rate with increasing value of predicted column, to be chosen among "increasing" or "decreasing" (default value is decreasing) |
An object of class "gini_table" is a list containing the following components:
prediction |
base with the predicted value as a dataframe |
gini_tab |
gini table as a dataframe |
gini_value |
gini coefficient value |
gini_plot |
gini curve plot |
ks_value |
Kolmogorov-Smirnov statistic |
breaks |
break points |
Arya Poddar <aryapoddar290990@gmail.com>
Aiana Goyal <aianagoel002@gmail.com>
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | data <- iris
data$Species <- as.character(data$Species)
suppressWarnings(RNGversion('3.5.0'))
set.seed(11)
data$Y <- sample(0:1,size=nrow(data),replace=TRUE)
suppressWarnings(RNGversion('3.5.0'))
set.seed(11)
data$Y_pred <- sample(300:900,size=nrow(data),replace=TRUE)
gini_tab_list <- gini_table(base = data,target = "Y",col_pred = "Y_pred",quantile_pt = 10)
gini_tab_list$prediction
gini_tab_list$gini_tab
gini_tab_list$gini_value
gini_tab_list$gini_plot
gini_tab_list$ks_value
gini_tab_list$breaks
|
Target predicted band
1 0 466 (452,499]
2 0 300 [300,373]
3 1 606 (602,658]
4 0 308 [300,373]
5 0 338 [300,373]
6 1 873 (789,896]
7 0 351 [300,373]
8 0 474 (452,499]
9 1 829 (789,896]
10 0 374 (373,420]
11 0 405 (373,420]
12 0 564 (560,602]
13 1 845 (789,896]
14 1 811 (789,896]
15 1 741 (717,789]
16 1 644 (602,658]
17 0 589 (560,602]
18 0 498 (452,499]
19 0 394 (373,420]
20 0 588 (560,602]
21 0 422 (420,452]
22 1 708 (658,717]
23 0 518 (499,560]
24 0 510 (499,560]
25 0 337 [300,373]
26 0 590 (560,602]
27 0 539 (499,560]
28 0 309 [300,373]
29 0 375 (373,420]
30 0 539 (499,560]
31 1 603 (602,658]
32 0 496 (452,499]
33 0 547 (499,560]
34 0 421 (420,452]
35 1 788 (717,789]
36 1 685 (658,717]
37 0 466 (452,499]
38 0 362 [300,373]
39 0 453 (452,499]
40 0 334 [300,373]
41 0 448 (420,452]
42 0 429 (420,452]
43 0 596 (560,602]
44 1 692 (658,717]
45 0 497 (452,499]
46 1 819 (789,896]
47 1 683 (658,717]
48 0 308 [300,373]
49 1 618 (602,658]
50 1 801 (789,896]
51 1 602 (560,602]
52 1 659 (658,717]
53 0 555 (499,560]
54 0 487 (452,499]
55 0 433 (420,452]
56 0 566 (560,602]
57 0 547 (499,560]
58 1 790 (789,896]
59 0 397 (373,420]
60 1 658 (602,658]
61 0 380 (373,420]
62 1 752 (717,789]
63 0 404 (373,420]
64 1 648 (602,658]
65 1 751 (717,789]
66 0 409 (373,420]
67 0 334 [300,373]
68 0 599 (560,602]
69 0 415 (373,420]
70 1 672 (658,717]
71 1 712 (658,717]
72 0 555 (499,560]
73 0 556 (499,560]
74 0 422 (420,452]
75 1 864 (789,896]
76 1 621 (602,658]
77 0 462 (452,499]
78 0 499 (499,560]
79 0 518 (499,560]
80 0 432 (420,452]
81 0 330 [300,373]
82 0 598 (560,602]
83 1 605 (602,658]
84 0 441 (420,452]
85 1 789 (717,789]
86 0 551 (499,560]
87 0 415 (373,420]
88 1 689 (658,717]
89 1 788 (717,789]
90 0 435 (420,452]
91 0 519 (499,560]
92 1 759 (717,789]
93 0 308 [300,373]
94 0 586 (560,602]
95 1 787 (717,789]
96 0 443 (420,452]
97 1 760 (717,789]
98 0 416 (373,420]
99 1 652 (602,658]
100 0 434 (420,452]
101 1 771 (717,789]
102 1 706 (658,717]
103 0 545 (499,560]
104 0 545 (499,560]
105 0 424 (420,452]
106 0 458 (452,499]
107 1 715 (658,717]
108 1 744 (717,789]
109 1 638 (602,658]
110 0 385 (373,420]
111 1 724 (717,789]
112 0 354 [300,373]
113 0 563 (560,602]
114 1 794 (789,896]
115 1 702 (658,717]
116 0 481 (452,499]
117 1 859 (789,896]
118 0 569 (560,602]
119 1 614 (602,658]
120 0 447 (420,452]
121 0 418 (373,420]
122 0 473 (452,499]
123 1 895 (789,896]
124 0 402 (373,420]
125 0 571 (560,602]
126 0 343 [300,373]
127 0 315 [300,373]
128 1 814 (789,896]
129 1 723 (717,789]
130 1 679 (658,717]
131 1 873 (789,896]
132 1 709 (658,717]
133 0 428 (420,452]
134 1 615 (602,658]
135 0 384 (373,420]
136 0 589 (560,602]
137 0 463 (452,499]
138 1 726 (717,789]
139 1 765 (717,789]
140 1 896 (789,896]
141 1 705 (658,717]
142 1 635 (602,658]
143 0 570 (560,602]
144 1 713 (658,717]
145 0 433 (420,452]
146 1 617 (602,658]
147 1 654 (602,658]
148 0 498 (452,499]
149 1 821 (789,896]
150 0 482 (452,499]
Lower_open_bound Upper_closed_bound Band Total Non_event Event
11 0 0 <NA> 0 0 0
1 [300 373 [300,373] 15 15 0
2 373 420 (373,420] 15 15 0
3 420 452 (420,452] 15 15 0
4 452 499 (452,499] 15 15 0
5 499 560 (499,560] 15 15 0
6 560 602 (560,602] 15 14 1
7 602 658 (602,658] 15 0 15
8 658 717 (658,717] 15 0 15
9 717 789 (717,789] 15 0 15
10 789 896 (789,896] 15 0 15
Event_rate Cuml_non_event Cuml_event Pop_perc Cuml_non_event_perc
11 0.00000000 0 0 0.0 0.0000000
1 0.00000000 15 0 0.1 0.1685393
2 0.00000000 30 0 0.1 0.3370787
3 0.00000000 45 0 0.1 0.5056180
4 0.00000000 60 0 0.1 0.6741573
5 0.00000000 75 0 0.1 0.8426966
6 0.06666667 89 1 0.1 1.0000000
7 1.00000000 89 16 0.1 1.0000000
8 1.00000000 89 31 0.1 1.0000000
9 1.00000000 89 46 0.1 1.0000000
10 1.00000000 89 61 0.1 1.0000000
Cuml_event_perc Diff GINI_formula
11 0.00000000 0.0000000 NA
1 0.00000000 -0.1685393 0.000000000
2 0.00000000 -0.3370787 0.000000000
3 0.00000000 -0.5056180 0.000000000
4 0.00000000 -0.6741573 0.000000000
5 0.00000000 -0.8426966 0.000000000
6 0.01639344 -0.9836066 0.001289372
7 0.26229508 -0.7377049 0.000000000
8 0.50819672 -0.4918033 0.000000000
9 0.75409836 -0.2459016 0.000000000
10 1.00000000 0.0000000 0.000000000
[1] -0.9974213
[1] 0
[1] 300.0 372.8 420.4 451.5 498.6 559.5 602.4 658.3 716.6 789.1 896.0
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.