Datasets:
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age
int32 30
83
| year_of_operation
int32 1.96k
1.97k
| number_of_axillary_nodes
int32 0
52
| has_survived_5_years
class label 2
classes |
|---|---|---|---|
30 | 1,964 | 1 | 1yes
|
30 | 1,962 | 3 | 1yes
|
30 | 1,965 | 0 | 1yes
|
31 | 1,959 | 2 | 1yes
|
31 | 1,965 | 4 | 1yes
|
33 | 1,958 | 10 | 1yes
|
33 | 1,960 | 0 | 1yes
|
34 | 1,959 | 0 | 0no
|
34 | 1,966 | 9 | 0no
|
34 | 1,958 | 30 | 1yes
|
34 | 1,960 | 1 | 1yes
|
34 | 1,961 | 10 | 1yes
|
34 | 1,967 | 7 | 1yes
|
34 | 1,960 | 0 | 1yes
|
35 | 1,964 | 13 | 1yes
|
35 | 1,963 | 0 | 1yes
|
36 | 1,960 | 1 | 1yes
|
36 | 1,969 | 0 | 1yes
|
37 | 1,960 | 0 | 1yes
|
37 | 1,963 | 0 | 1yes
|
37 | 1,958 | 0 | 1yes
|
37 | 1,959 | 6 | 1yes
|
37 | 1,960 | 15 | 1yes
|
37 | 1,963 | 0 | 1yes
|
38 | 1,969 | 21 | 0no
|
38 | 1,959 | 2 | 1yes
|
38 | 1,960 | 0 | 1yes
|
38 | 1,960 | 0 | 1yes
|
38 | 1,962 | 3 | 1yes
|
38 | 1,964 | 1 | 1yes
|
38 | 1,966 | 0 | 1yes
|
38 | 1,966 | 11 | 1yes
|
38 | 1,960 | 1 | 1yes
|
38 | 1,967 | 5 | 1yes
|
39 | 1,966 | 0 | 0no
|
39 | 1,963 | 0 | 1yes
|
39 | 1,967 | 0 | 1yes
|
39 | 1,958 | 0 | 1yes
|
39 | 1,959 | 2 | 1yes
|
39 | 1,963 | 4 | 1yes
|
40 | 1,958 | 2 | 1yes
|
40 | 1,958 | 0 | 1yes
|
40 | 1,965 | 0 | 1yes
|
41 | 1,960 | 23 | 0no
|
41 | 1,964 | 0 | 0no
|
41 | 1,967 | 0 | 0no
|
41 | 1,958 | 0 | 1yes
|
41 | 1,959 | 8 | 1yes
|
41 | 1,959 | 0 | 1yes
|
41 | 1,964 | 0 | 1yes
|
41 | 1,969 | 8 | 1yes
|
41 | 1,965 | 0 | 1yes
|
41 | 1,965 | 0 | 1yes
|
42 | 1,969 | 1 | 0no
|
42 | 1,959 | 0 | 0no
|
42 | 1,958 | 0 | 1yes
|
42 | 1,960 | 1 | 1yes
|
42 | 1,959 | 2 | 1yes
|
42 | 1,961 | 4 | 1yes
|
42 | 1,962 | 20 | 1yes
|
42 | 1,965 | 0 | 1yes
|
42 | 1,963 | 1 | 1yes
|
43 | 1,958 | 52 | 0no
|
43 | 1,959 | 2 | 0no
|
43 | 1,964 | 0 | 0no
|
43 | 1,964 | 0 | 0no
|
43 | 1,963 | 14 | 1yes
|
43 | 1,964 | 2 | 1yes
|
43 | 1,964 | 3 | 1yes
|
43 | 1,960 | 0 | 1yes
|
43 | 1,963 | 2 | 1yes
|
43 | 1,965 | 0 | 1yes
|
43 | 1,966 | 4 | 1yes
|
44 | 1,964 | 6 | 0no
|
44 | 1,958 | 9 | 0no
|
44 | 1,963 | 19 | 0no
|
44 | 1,961 | 0 | 1yes
|
44 | 1,963 | 1 | 1yes
|
44 | 1,961 | 0 | 1yes
|
44 | 1,967 | 16 | 1yes
|
45 | 1,965 | 6 | 0no
|
45 | 1,966 | 0 | 0no
|
45 | 1,967 | 1 | 0no
|
45 | 1,960 | 0 | 1yes
|
45 | 1,967 | 0 | 1yes
|
45 | 1,959 | 14 | 1yes
|
45 | 1,964 | 0 | 1yes
|
45 | 1,968 | 0 | 1yes
|
45 | 1,967 | 1 | 1yes
|
46 | 1,958 | 2 | 0no
|
46 | 1,969 | 3 | 0no
|
46 | 1,962 | 5 | 0no
|
46 | 1,965 | 20 | 0no
|
46 | 1,962 | 0 | 1yes
|
46 | 1,958 | 3 | 1yes
|
46 | 1,963 | 0 | 1yes
|
47 | 1,963 | 23 | 0no
|
47 | 1,962 | 0 | 0no
|
47 | 1,965 | 0 | 0no
|
47 | 1,961 | 0 | 1yes
|
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Haberman
The Haberman dataset from the UCI ML repository. Has the patient survived surgery?
Configurations and tasks
| Configuration | Task | Description |
|---|---|---|
| sruvival | Binary classification | Has the patient survived surgery? |
Usage
from datasets import load_dataset
dataset = load_dataset("mstz/haberman", "survival")["train"]
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