DOC replace AUPCR by PRC

lorentzenchr · lorentzenchr · commit 031082592606 · 2023-01-13T22:36:54.000+01:00
diff --git a/site/en/tutorials/structured_data/imbalanced_data.ipynb b/site/en/tutorials/structured_data/imbalanced_data.ipynb
@@ -1046,7 +1046,7 @@
         "id": "Y5twGRLfNwmO"
       },
       "source": [
-        "### Plot the AUPRC\n",
+        "### Plot the PRC\n",
         "\n",
         "Now plot the [AUPRC](https://developers.google.com/machine-learning/glossary?hl=en#PR_AUC). Area under the interpolated precision-recall curve, obtained by plotting (recall, precision) points for different values of the classification threshold. Depending on how it's calculated, PR AUC may be equivalent to the average precision of the model.\n"
       ]
@@ -1268,7 +1268,7 @@
         "id": "_0krS8g1OTbD"
       },
       "source": [
-        "### Plot the AUPRC"
+        "### Plot the PRC"
       ]
     },
     {

Original file line number	Diff line number	Diff line change
`@@ -1046,7 +1046,7 @@`
`1046`	`1046`	`"id": "Y5twGRLfNwmO"`
`1047`	`1047`	`},`
`1048`	`1048`	`"source": [`
`1049`		`- "### Plot the AUPRC\n",`
	`1049`	`+ "### Plot the PRC\n",`
`1050`	`1050`	`"\n",`
`1051`	`1051`	`"Now plot the [AUPRC](https://developers.google.com/machine-learning/glossary?hl=en#PR_AUC). Area under the interpolated precision-recall curve, obtained by plotting (recall, precision) points for different values of the classification threshold. Depending on how it's calculated, PR AUC may be equivalent to the average precision of the model.\n"`
`1052`	`1052`	`]`
`@@ -1268,7 +1268,7 @@`
`1268`	`1268`	`"id": "_0krS8g1OTbD"`
`1269`	`1269`	`},`
`1270`	`1270`	`"source": [`
`1271`		`- "### Plot the AUPRC"`
	`1271`	`+ "### Plot the PRC"`
`1272`	`1272`	`]`
`1273`	`1273`	`},`
`1274`	`1274`	`{`