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The pos/neg/facts setup feels limiting when a dataset has multiple potential targets.
Consider imdb—where female_gender and workedunder are common targets.
imdb
female_gender
workedunder
There's usually a target in mind, but you can setup the problem with any target you like.
Side Note:
The following setup prescribes predicting y using X.
y
X
from sklearn.datasets import load_breast_cancer X, y = load_breast_cancer(return_X_y=True)
But the vectors can be re-ordered to to predict the third column X[:, 3] after substituting y with the third column.
X[:, 3]
The text was updated successfully, but these errors were encountered:
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The pos/neg/facts setup feels limiting when a dataset has multiple potential targets.
Consider
imdb
—wherefemale_gender
andworkedunder
are common targets.There's usually a target in mind, but you can setup the problem with any target you like.
Side Note:
The following setup prescribes predicting
y
usingX
.But the vectors can be re-ordered to to predict the third column
X[:, 3]
after substituting y with the third column.The text was updated successfully, but these errors were encountered: