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docs: use class_weight="balanced" in the logistic regression prediction tutorial #678

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Merged
merged 4 commits into from
May 16, 2024
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docs: use class_weight="balanced" in the logistic regression prediction tutorial #678

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docs: use `class_weight="balanced"` in the logistic regression predi…
tswast May 10, 2024
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Merge branch 'main' into tswast-patch-1
tswast May 13, 2024
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Merge branch 'main' into tswast-patch-1
tswast May 14, 2024
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Merge branch 'main' into tswast-patch-1
tswast May 15, 2024
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16 changes: 15 additions & 1 deletion 16 samples/snippets/logistic_regression_prediction_test.py
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Expand Up @@ -80,7 +80,21 @@ def test_logistic_regression_prediction(random_model_id: str) -> None:
X = training_data.drop(columns=["income_bracket", "dataframe"])
y = training_data["income_bracket"]

census_model = bigframes.ml.linear_model.LogisticRegression()
census_model = bigframes.ml.linear_model.LogisticRegression(
# Balance the class labels in the training data by setting
# class_weight="balanced".
#
# By default, the training data is unweighted. If the labels
# in the training data are imbalanced, the model may learn to
# predict the most popular class of labels more heavily. In
# this case, most of the respondents in the dataset are in the
# lower income bracket. This may lead to a model that predicts
# the lower income bracket too heavily. Class weights balance
# the class labels by calculating the weights for each class in
# inverse proportion to the frequency of that class.
class_weight="balanced",
max_iterations=15,
)
census_model.fit(X, y)

census_model.to_gbq(
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