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Clean CSVs with one right answer Copying the instructor’s cell Accuracy on a balanced toy set A certificate nobody reads
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subtype_classification.ipynb
Step 3 — Exploratory analysis
Understand the data before you touch a model.
Understand the data before you touch a model.
[1]
import pandas as pd
df = pd.read_csv('expression.csv')
print(df.shape)
print(df['subtype'].value_counts())
Out [1]
(523, 1842)0 441 ← 84.3%
1 58 ← 11.1%
2 24 ← 4.6%
[ ]
# your call — what next?
Klerisy Tutor
Gene expression · subtypes
Klerisy
523 samples, 1,842 features. What does that ratio tell you before you model anything?
You
p ≫ n. I’d reduce dimensions first.
Klerisy
Good instinct. But 84% of your samples are one class. Which metric would make a useless model look brilliant here?
Hint: think about what a majority-class predictor scores.
↑
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You finished the course.
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35% missing and no documentation Choosing between three defensible paths Knowing why your metric is lying Explaining it to someone senior
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Step 01
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Step 02
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Step 03
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Two people. Same missing piece.
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Three things, and none
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