Briefly explain how these two plots are similar but also how they are different. Compare malignant and benign tumors in terms of location, spread, and skew for the area_worst measurement. Briefly explain what we have learned about cancerous and non-cancerous tumors from these plots.
Briefly explain how these two plots are similar but also how they are different. Compare malignant and benign tumors in terms of location, spread, and skew for the area_worst measurement. Briefly explain what we have learned about cancerous and non-cancerous tumors from these plots.
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Transcribed Image Text:For each tumor, we can learn the size of the largest cell (in terms of
area) using the "area_worst" column. Here are two plots that show
"area_worst" broken out by whether the tumor was cancerous (M) or
not (B).
sb.histplot (data = cancer, x = "area_worst", hue = "Diagnosis")
<Axes: xlabel='area_worst', ylabel='Count'>
Count
120
Density
100
80
60
40
20
0-
0
▸ sb.kdeplot(data = cancer, x = "area_worst", hue = "Diagnosis", fill = True
--INSERT--
<Axes: xlabel='area_worst', ylabel='Density'>
0.0016
0.0014
0.0012
0.0010
0.0008-
0.0006
0.0004-
0.0002
0.0000
500 1000 1500 2000 2500 3000 3500 4000
area_worst
0
1000
2000
3000
Diagnosis
M
B
area_worst
4000
Diagnosis
M
B
5000
Transcribed Image Text:Briefly explain how these two plots are similar but also how they are
different.
Compare malignant and benign tumors in terms of location, spread,
and skew for the area_worst measurement. Briefly explain what we
have learned about cancerous and non-cancerous tumors from these
plots.
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