From ebdcf0edaac5c1b07867ceb5269be12a4b1257dd Mon Sep 17 00:00:00 2001 From: Jen Looper Date: Wed, 28 Jul 2021 23:08:47 -0400 Subject: [PATCH] Update README.md --- 2-Regression/4-Logistic/README.md | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/2-Regression/4-Logistic/README.md b/2-Regression/4-Logistic/README.md index 2f31b6ed4..0708b3653 100644 --- a/2-Regression/4-Logistic/README.md +++ b/2-Regression/4-Logistic/README.md @@ -227,9 +227,6 @@ What's going on here? Let's say our model is asked to classify items between two - If your model predicts something as a pumpkin and it belongs to category 'not-a-pumpkin' in reality we call it a false negative, shown by the bottom left number. - If your model predicts something as not a pumpkin and it belongs to category 'not-a-pumpkin' in reality we call it a true negative, shown by the bottom right number. -![Confusion Matrix](images/confusion-matrix.png) - -> Infographic by [Jen Looper](https://twitter.com/jenlooper) 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. @@ -285,6 +282,7 @@ In future lessons on classifications, you will learn how to iterate to improve y ## 🚀Challenge There's a lot more to unpack regarding logistic regression! But the best way to learn is to experiment. Find a dataset that lends itself to this type of analysis and build a model with it. What do you learn? tip: try [Kaggle](https://www.kaggle.com/search?q=logistic+regression+datasets) for interesting datasets. + ## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/16/) ## Review & Self Study