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[Minor] [ML] Use coefficients replace weights #10065

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[Minor] [ML] Use coefficients replace weights #10065

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2 changes: 1 addition & 1 deletion python/pyspark/ml/classification.py
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Expand Up @@ -49,7 +49,7 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti
... Row(label=0.0, weight=2.0, features=Vectors.sparse(1, [], []))]).toDF()
>>> lr = LogisticRegression(maxIter=5, regParam=0.01, weightCol="weight")
>>> model = lr.fit(df)
>>> model.weights
>>> model.coefficients
DenseVector([5.5...])
>>> model.intercept
-2.68...
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2 changes: 1 addition & 1 deletion python/pyspark/ml/regression.py
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Expand Up @@ -40,7 +40,7 @@ class LinearRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPrediction
Linear regression.

The learning objective is to minimize the squared error, with regularization.
The specific squared error loss function used is: L = 1/2n ||A weights - y||^2^
The specific squared error loss function used is: L = 1/2n ||A coefficients - y||^2^

This support multiple types of regularization:
- none (a.k.a. ordinary least squares)
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