From 241d966e54c77e0af609d183aea2b0e45cb6a726 Mon Sep 17 00:00:00 2001 From: Ashwin Aishvarya Vardhan Date: Fri, 17 May 2019 20:56:21 +0530 Subject: [PATCH] added info about scikit-learn and accuracy metrics (#30179) * added info about scikit-learn and accuracy metrics Added some information about R-squared score. * Update index.md --- guide/english/machine-learning/linear-regression/index.md | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/guide/english/machine-learning/linear-regression/index.md b/guide/english/machine-learning/linear-regression/index.md index 28209a1d55..bdc53e430c 100644 --- a/guide/english/machine-learning/linear-regression/index.md +++ b/guide/english/machine-learning/linear-regression/index.md @@ -76,6 +76,10 @@ y_pred_class = model.predict(X_test) print(metrics.accuracy_score(y_test, y_pred_class)) ``` +## Metrics +How would you know if your regression model is accurate or not? Well, one method is to calculate the R-squared score of your model. [R-squared score](http://blog.minitab.com/blog/adventures-in-statistics-2/regression-analysis-how-do-i-interpret-r-squared-and-assess-the-goodness-of-fit) (also known as coefficient of determination) is a statistical measure of how close the data are to the fitted regression line. + +## More Information: +[scikit learn](http://scikit-learn.org/stable/documentation.html) +[Article on regression] https://www.analyticsvidhya.com/blog/2017/06/a-comprehensive-guide-for-linear-ridge-and-lasso-regression/ -You can refer to this article for deeper insight into regression -https://www.analyticsvidhya.com/blog/2017/06/a-comprehensive-guide-for-linear-ridge-and-lasso-regression/