Predicting the Amount of Compensation for Harm Awarded by Courts Using Machine-Learning Algorithms

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Tokens Characterized by the Highest Differences Between Maximum and Minimum Value of Their Partial Dependence Plots

Id	Token	Average awarded compensation when token does not occur	Minimum awarded compensation	Maximum awarded compensation	Difference between maximum and minimum awarded compensation
1	pension	21,027.80	21,027.80 (0)	38,725.99 (5)	17,698.18
2	family	16,532.26	16,532.26 (0)	34,000.38 (16)	17,468.12
3	hospital	20,559.30	20,559.30 (0)	35,327.96 (20)	14,768.65
4	fracture	19,852.62	19,852.62 (0)	30,671.07 (25)	10,818.46
5	year	19,473.51	19,473.51 (0)	28,048.15 (10)	8,574.65
6	life	19,712.14	19,712.14 (0)	25,905.34 (36)	6,193.20
7	dead	22,645.50	22,645.50 (0)	28,181.21 (7)	5,535.71
8	zloty_monthly	22,557.29	22,557.29 (0)	27,708.96 (29)	5,151.67
9	extend	22,570.76	22,570.76 (0)	26,658.50 (6)	4,087.74
10	substantial	22,346.35	22,346.35 (0)	25,140.15 (19)	2,793.80
11	bones	23,056.96	23,056.96 (0)	25,536.80 (25)	2,479.84
12	child	22,439.85	22,439.85 (0)	24,908.14 (39)	2,468.29
13	bond	23,108.54	23,108.54 (0)	25,423.49 (24)	2,314.95
14	cervical	23,675.45	21,368.98 (22)	23,675.45 (0)	2,306.47
15	disorders	22,655.92	22,655.92 (0)	24,947.08 (17)	2,291.17
16	collar	23,419.00	22,944.30 (11)	23,419.00 (0)	2,264.04
17	disability	22,944.30	22,944.30 (0)	25,129.94 (5)	2,185.64
18	family_bond	23,262.10	23,262.10 (0)	25,430.35 (10)	2,168.25
19	twist	23,417.04	21,541.70 (7)	23,417.04 (0)	1,875.34
20	future	22,640.79	22,640.79 (0)	24,386.69 (22)	1,745.90

Error Measures Obtained on a Test Set With Different Algorithms Applied

Algorithm applied	Predictors	Root mean squared error	Mean Absolute percentage error	Root median squared error	Median absolute percentage error
OLS	Token counts	308,207.87	112.32	11,747.55	52.79
	TF-IDF	100,743.48	94.59	11,759.07	49.25
LASSO	Token counts	287,706.24	109.68	11,474.52	52.64
	TF-IDF	95,976.82	93.46	11,435.79	48.64
Random forests	Token counts	75,268.12	86.43	11,064.28	47.16
	TF-IDF	74,674.55	88.59	10,518.75	48.13
XGBoost	Token counts	94,271.95	105.27	12,654.46	52.18
	TF-IDF	192,230.66	102.79	12,821.45	51.48
Multilingual BERT	-	95,665.94	426.45	38,062.44	88.03

Error Measures Obtained on a Test Set With Different Algorithms Applied and Models’ Overestimates Assumed as Not Errors

Algorithm applied	Predictors	Root mean squared error	Mean Absolute percentage error	Root median squared error	Median absolute percentage error
OLS	Token counts	61,829.04	23.92	1,024.79	5.29
	TF-IDF	67,563.77	21.83	178.39	1.08
LASSO	Token counts	62,019.89	23.83	1,061.22	5.57
	TF-IDF	68,215.32	21.78	129.66	0.69
Random forests	Token counts	74,939.24	23.11	1,729.41	8.01
	TF-IDF	73,939.24	22.80	1,741.93	9.02
XGBoost	Token counts	71,414.42	23.83	1,069.24	7.93
	TF-IDF	65,657.99	24.13	1,209.66	7.68
Multilingual BERT	-	82,212.22	29.04	0.00	0.00