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docs: add linear model example #1235

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shuoweil merged 12 commits into from
Dec 23, 2024
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docs: add linear model example #1235

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aa0546b
docs: add linear model example
shuoweil Dec 20, 2024
dbbc0db
Merge branch 'main' into shuowei-samples
Dec 20, 2024
4070840
chore: add experimental series.blob.display() function (#1232)
GarrettWu Dec 20, 2024
b5cae99
add an extra setting to set display prgress bar to None
shuoweil Dec 20, 2024
4931ccb
Merge branch 'main' into shuowei-samples
Dec 20, 2024
8a7f9c5
docs: add KMeans example (#1234)
GarrettWu Dec 20, 2024
0eded65
docs: add examples for ml PCA and SimpleImputer (#1236)
GarrettWu Dec 21, 2024
d4b0cbe
fix doctest bug
shuoweil Dec 21, 2024
dc96afc
Merge branch 'main' into shuowei-samples
Dec 21, 2024
929cf38
add extra lines to fix docs bug
shuoweil Dec 23, 2024
2f49963
fix docstring bug
shuoweil Dec 23, 2024
706ddf2
remove an extra line
shuoweil Dec 23, 2024
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24 changes: 24 additions & 0 deletions third_party/bigframes_vendored/sklearn/linear_model/_base.py
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Expand Up @@ -62,6 +62,30 @@ class LinearRegression(RegressorMixin, LinearModel):
to minimize the residual sum of squares between the observed targets in
the dataset, and the targets predicted by the linear approximation.

**Examples:**

>>> from bigframes.ml.linear_model import LinearRegression
>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None
>>> X = bpd.DataFrame({ \
"feature0": [20, 21, 19, 18], \
"feature1": [0, 1, 1, 0], \
"feature2": [0.2, 0.3, 0.4, 0.5]})
>>> y = bpd.DataFrame({"outcome": [0, 0, 1, 1]})
>>> # Create the linear model
>>> model = LinearRegression()
>>> model.fit(X, y)
LinearRegression()

>>> # Score the model
>>> score = model.score(X, y)
>>> print(score) # doctest:+SKIP
mean_absolute_error mean_squared_error mean_squared_log_error \
0 0.022812 0.000602 0.00035
median_absolute_error r2_score explained_variance
0 0.015077 0.997591 0.997591


Args:
optimize_strategy (str, default "auto_strategy"):
The strategy to train linear regression models. Possible values are
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