8000 FIX Use slinear interpolation for isotonic regression · scikit-learn/scikit-learn@a9ea55f · GitHub
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FIX Use slinear interpolation for isotonic regression
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sklearn/isotonic.py

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< 5C92 td data-grid-cell-id="diff-d4c985943c9741254b4d1091580227c07e34a95f7b48f4792d6b3b1874484f43-229-229-2" data-line-anchor="diff-d4c985943c9741254b4d1091580227c07e34a95f7b48f4792d6b3b1874484f43R229" data-selected="false" role="gridcell" style="background-color:var(--bgColor-default);padding-right:24px" tabindex="-1" valign="top" class="focusable-grid-cell diff-text-cell right-side-diff-cell left-side">

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@@ -228,7 +228,7 @@ def _build_f(self, X, y):
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.format(self.out_of_bounds))
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bounds_error = self.out_of_bounds == "raise"
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self.f_ = interpolate.interp1d(X, y, kind='linear',
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self.f_ = interpolate.interp1d(X, y, kind='slinear',
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bounds_error=bounds_error)
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def _build_y(self, X, y, sample_weight):

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