From f838fb601a2aa165448e8eedb4cd53ca29c3c2a8 Mon Sep 17 00:00:00 2001 From: know-one-1 Date: Sat, 11 Dec 2021 17:30:44 +0530 Subject: [PATCH] changed the definition of false positive and false negative --- 2-Regression/4-Logistic/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/2-Regression/4-Logistic/README.md b/2-Regression/4-Logistic/README.md index 9ff52164..0e57022c 100644 --- a/2-Regression/4-Logistic/README.md +++ b/2-Regression/4-Logistic/README.md @@ -232,8 +232,8 @@ In Scikit-learn, confusion matrices Rows (axis 0) are actual labels and columns What's going on here? Let's say our model is asked to classify pumpkins between two binary categories, category 'orange' and category 'not-orange'. - If your model predicts a pumpkin as not orange and it belongs to category 'not-orange' in reality we call it a true negative, shown by the top left number. -- If your model predicts a pumpkin as orange and it belongs to category 'not-orange' in reality we call it a false negative, shown by the bottom left number. -- If your model predicts a pumpkin as not orange and it belongs to category 'orange' in reality we call it a false positive, shown by the top right number. +- If your model predicts a pumpkin as orange and it belongs to category 'not-orange' in reality we call it a false positive, shown by the bottom left number. +- If your model predicts a pumpkin as not orange and it belongs to category 'orange' in reality we call it a false negative, shown by the top right number. - If your model predicts a pumpkin as orange and it belongs to category 'orange' in reality we call it a true positive, shown by the bottom right number. As you might have guessed it's preferable to have a larger number of true positives and true negatives and a lower number of false positives and false negatives, which implies that the model performs better.