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Docs: fix example; export MinPrecisionThreshold
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@@ -197,7 +197,7 @@ while raising a component's threshold makes it more conservative. </p>
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to achieve a minimum desired true positive rate (also known as precision). The example below shows how to initialize
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to achieve a minimum desired true positive rate (also known as precision). The example below shows how to initialize
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a <code>PrimalSolutionComponent</code> which achieves 95% precision, possibly at the cost of a lower recall. To make the component
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a <code>PrimalSolutionComponent</code> which achieves 95% precision, possibly at the cost of a lower recall. To make the component
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more aggressive, this precision may be lowered.</p>
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more aggressive, this precision may be lowered.</p>
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<pre><code class="python">comp = PrimalSolutionComponent(threshold=MinPrecisionThreshold(0.98))
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<pre><code class="python">PrimalSolutionComponent(threshold=MinPrecisionThreshold(0.95))
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</code></pre></div>
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</code></pre></div>
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@@ -273,5 +273,5 @@
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<!--
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<!--
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MkDocs version : 1.1
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MkDocs version : 1.1
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Build Date UTC : 2020-05-05 18:08:32
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Build Date UTC : 2020-05-05 18:12:23
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-->
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-->
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@@ -59,5 +59,5 @@ a `PrimalSolutionComponent` which achieves 95% precision, possibly at the cost o
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more aggressive, this precision may be lowered.
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more aggressive, this precision may be lowered.
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```python
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```python
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comp = PrimalSolutionComponent(threshold=MinPrecisionThreshold(0.98))
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PrimalSolutionComponent(threshold=MinPrecisionThreshold(0.95))
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```
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```
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@@ -14,6 +14,7 @@ from .components.primal import PrimalSolutionComponent
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from .components.branching import BranchPriorityComponent, BranchPriorityExtractor
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from .components.branching import BranchPriorityComponent, BranchPriorityExtractor
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from .classifiers.adaptive import AdaptiveClassifier
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from .classifiers.adaptive import AdaptiveClassifier
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from .classifiers.threshold import MinPrecisionThreshold
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from .benchmark import BenchmarkRunner
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from .benchmark import BenchmarkRunner
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