From 95f1fa467fcc56104c342e6c279647915d2e5681 Mon Sep 17 00:00:00 2001
From: Mikolaj Rybinski <mikolaj.rybinski@id.ethz.ch>
Date: Mon, 6 May 2019 08:46:38 +0200
Subject: [PATCH] metrics: fix solution bug
---
04_measuring_quality_of_a_classifier.ipynb | 5 +++--
1 file changed, 3 insertions(+), 2 deletions(-)
diff --git a/04_measuring_quality_of_a_classifier.ipynb b/04_measuring_quality_of_a_classifier.ipynb
index 8597d27..3380a08 100644
--- a/04_measuring_quality_of_a_classifier.ipynb
+++ b/04_measuring_quality_of_a_classifier.ipynb
@@ -312,7 +312,7 @@
"source": [
"## Exercise block 1\n",
"\n",
- "1.1 A classifier predicts labels `[0, 0, 1, 1, 1, 0, 1, 1]` whereas true labels are `[0, 1, 0, 1, 1, 0, 1, 0]`. First write these values as a two columned table using pen & paper and assign `FP`, `TP`, ... to each row. Now create the confusion matrix and compute accuracy.\n",
+ "1.1 A classifier predicts labels `[0, 1, 0, 1, 1, 0, 1, 0]` whereas true labels are `[0, 0, 1, 1, 1, 0, 1, 1]`. First write these values as a two columned table using pen & paper and assign `FP`, `TP`, ... to each row. Now create the confusion matrix and compute accuracy.\n",
"\n",
"1.2 A random classfier just assign a randomly chosen label `0` or `1` for a given feature. What is the average accuracy of such a classifier?"
]
@@ -809,6 +809,7 @@
}
],
"metadata": {
+ "celltoolbar": "Tags",
"kernelspec": {
"display_name": "Python 3",
"language": "python",
@@ -824,7 +825,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.2"
+ "version": "3.7.3"
},
"latex_envs": {
"LaTeX_envs_menu_present": true,
--
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