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Describe the issue: Unable to print logistic regression score as a float instead get an empty array:
ArrayChunkBytes8 B8 BShape()()Dask graph1 chunks in 32 graph layersData typefloat64 numpy.ndarray | Array | Chunk | Bytes | 8 B | 8 B | Shape | () | () | Dask graph | 1 chunks in 32 graph layers | Data type | float64 numpy.ndarray
8 B | 8 B () | () 1 chunks in 32 graph layers float64 numpy.ndarray
Minimal Complete Verifiable Example:
# Put your MCVE code here ```from dask_ml.linear_model import LogisticRegression from dask_glm.datasets import make_classification X, y = make_classification() lr = LogisticRegression() lr.fit(X, y) lr.score(X, y) **Anything else we need to know?**: **Environment**: - Dask version:'2023.4.1' - Python version:3.9 - Operating System:Mac OS - Install method (conda, pip, source): Conda
The text was updated successfully, but these errors were encountered:
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Describe the issue:
Unable to print logistic regression score as a float instead get an empty array:
ArrayChunkBytes8 B8 BShape()()Dask graph1 chunks in 32 graph layersData typefloat64 numpy.ndarray | Array | Chunk | Bytes | 8 B | 8 B | Shape | () | () | Dask graph | 1 chunks in 32 graph layers | Data type | float64 numpy.ndarray
8 B | 8 B
() | ()
1 chunks in 32 graph layers
float64 numpy.ndarray
Minimal Complete Verifiable Example:
The text was updated successfully, but these errors were encountered: