From 6d5b4205df02830c2ffd8e07110e331255a596b5 Mon Sep 17 00:00:00 2001 From: cfiutak1 Date: Tue, 2 Apr 2019 01:04:43 -0400 Subject: [PATCH] Update Part 1 - Introduction to Machine Learning with scikit-learn.md --- Part 1 - Introduction to Machine Learning with scikit-learn.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Part 1 - Introduction to Machine Learning with scikit-learn.md b/Part 1 - Introduction to Machine Learning with scikit-learn.md index 73ac23f..76f8fd0 100644 --- a/Part 1 - Introduction to Machine Learning with scikit-learn.md +++ b/Part 1 - Introduction to Machine Learning with scikit-learn.md @@ -53,7 +53,7 @@ You can find other useful datasets in the [official scikit-learn documentation]( Now, we're going to split the data into two sets - a training set and a testing set. The training set will be used to train the machine learning algorithms, whereas the testing set will be used to verify the accuracy of the machine learning algorithms. -To better visualize this relationship, think of a time where you studied for a math exam by completing practice problems. Then, you tested your knowledge by completing the exam. The practice problems you completed were your training set, and the real exam was the testing set. +To better visualize this relationship, think of a time where you studied for a math exam by completing practice problems, and tested your knowledge by completing the exam. The practice problems you completed were your training set, and the real exam was the testing set. ⚠ **It is imperative that you keep your training and testing sets separate during the training process** - if your machine learning algorithm is tested with a data point it's already seen before, it may report a testing accuracy that is higher than it actually is.