vault backup: 2026-05-21 14:53:37
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@@ -17,6 +17,6 @@
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"repelStrength": 10,
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"repelStrength": 10,
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"linkStrength": 1,
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"linkStrength": 1,
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"linkDistance": 250,
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"linkDistance": 250,
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"scale": 0.4985514747737353,
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"scale": 0.20021871622586232,
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"close": true
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"close": true
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}
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}
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@@ -16,26 +16,134 @@ tags: [excalidraw]
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%%
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%%
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## Drawing
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## Drawing
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```compressed-json
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```compressed-json
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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|
||||||
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||||||
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|
||||||
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||||||
|
|
||||||
|
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||||||
|
|
||||||
|
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||||||
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|
||||||
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||||||
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|
||||||
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||||||
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||||||
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||||||
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|
||||||
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||||||
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|
||||||
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||||||
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|
||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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|
||||||
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||||||
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|
||||||
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||||||
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|
||||||
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||||||
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|
||||||
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||||||
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|
||||||
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||||||
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|
||||||
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||||||
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|
||||||
|
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|
||||||
|
|
||||||
|
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|
||||||
```
|
```
|
||||||
%%
|
%%
|
||||||
+3
@@ -82,3 +82,6 @@ Log loss is used to optimize the weights of a logistic regression. MSE cannot be
|
|||||||
Logistic regressions also only predict between 0 - 1 (probabilities) so any error value will be between 0 - 1 using MSE which is not ideal.
|
Logistic regressions also only predict between 0 - 1 (probabilities) so any error value will be between 0 - 1 using MSE which is not ideal.
|
||||||
|
|
||||||
The log loss function is used to penalize wrong predictions more harshly when they are more confident.
|
The log loss function is used to penalize wrong predictions more harshly when they are more confident.
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![[LogLoss.excalidraw]]
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Reference in new issue
Block a user