From d32370b5ea408dd85ef0a20309e9d3ab4bcf1761 Mon Sep 17 00:00:00 2001 From: Suraj M S Date: Thu, 14 May 2020 22:18:12 +0100 Subject: [PATCH] Update readdme with nit fix. Fixed wording. --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index d191d52..045ba02 100644 --- a/README.md +++ b/README.md @@ -131,7 +131,7 @@ Cross-validation is a technique to evaluate predictive models by partitioning th ### Mean Squared Error vs. Mean Absolute Error * **Similarity**: both measure the average model prediction error; range from 0 to infinity; the lower the better -* Mean Squared Error (MSE) gives higher weights to large error (e.g., being off by 10 just MORE THAN TWICE as bad as being off by 5), whereas Mean Absolute Error (MAE) assign equal weights (being off by 10 is just twice as bad as being off by 5) +* Mean Squared Error (MSE) gives higher weights to large error (e.g., being off by 10 is MORE THAN TWICE as bad as being off by 5), whereas Mean Absolute Error (MAE) assign equal weights (being off by 10 is just twice as bad as being off by 5) * MSE is continuously differentiable, MAE is not (where y_pred == y_true) [back to top](#data-science-question-answer)