From 94f517a998c7cd6c985ca142cc54e43aa5b5ce74 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Mon, 6 Nov 2023 19:58:15 +0000 Subject: [PATCH 1/2] [pre-commit.ci] pre-commit autoupdate MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit updates: - [github.com/pre-commit/pre-commit-hooks: v4.4.0 → v4.5.0](https://github.com/pre-commit/pre-commit-hooks/compare/v4.4.0...v4.5.0) - [github.com/codespell-project/codespell: v2.2.5 → v2.2.6](https://github.com/codespell-project/codespell/compare/v2.2.5...v2.2.6) --- .pre-commit-config.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 18b19db..00b7334 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -11,7 +11,7 @@ ci: repos: - repo: https://github.com/pre-commit/pre-commit-hooks - rev: v4.4.0 + rev: v4.5.0 hooks: - id: check-added-large-files - id: check-case-conflict @@ -25,7 +25,7 @@ repos: files: ^(_episodes|code|README.md|setup.md) - repo: https://github.com/codespell-project/codespell - rev: "v2.2.5" + rev: "v2.2.6" hooks: - id: codespell args: ["-I", "codespell.txt"] From 17f4909c64d0aea7af8b5aa0240d5f92cc1bd6e6 Mon Sep 17 00:00:00 2001 From: Wouter Deconinck Date: Fri, 5 Jan 2024 15:04:12 -0600 Subject: [PATCH 2/2] fix: parameteric -> parametric (codespell) --- _episodes/11-Model_Comparison.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_episodes/11-Model_Comparison.md b/_episodes/11-Model_Comparison.md index ecab06d..bf0b7bc 100644 --- a/_episodes/11-Model_Comparison.md +++ b/_episodes/11-Model_Comparison.md @@ -102,7 +102,7 @@ decisions_nn = ( {: .language-python} # The ROC Curve -The Receiver Operating Characteristic (ROC) curve is a plot of the recall (or true positive rate) vs. the false positive rate: the ratio of negative instances incorrectly classified as positive. A classifier may classify many instances as positive (i.e. has a low tolerance for classifying something as positive), but in such an example it will probably also incorrectly classify many negative instances as positive as well. The false positive rate is plotted on the x-axis of the ROC curve and the true positive rate on the y-axis; the threshold is varied to give a parameteric curve. A random classifier results in a line. Before we look at the ROC curve, let's examine the following plot +The Receiver Operating Characteristic (ROC) curve is a plot of the recall (or true positive rate) vs. the false positive rate: the ratio of negative instances incorrectly classified as positive. A classifier may classify many instances as positive (i.e. has a low tolerance for classifying something as positive), but in such an example it will probably also incorrectly classify many negative instances as positive as well. The false positive rate is plotted on the x-axis of the ROC curve and the true positive rate on the y-axis; the threshold is varied to give a parametric curve. A random classifier results in a line. Before we look at the ROC curve, let's examine the following plot ~~~ plt.hist(