Understanding Confusion Matrices

Eli Bendersky explains what it is a confusion matrix tells us:

Now comes our first batch of definitions.

  • True positive (TP): Positive test result matches reality — the person is actually sick and tested positive.
  • False positive (FP): Positive test result doesn’t match reality — the test is positive but the person is not actually sick.
  • True negative (TN): Negative test result matches reality — the person is not sick and tested negative.
  • False negative (FN): Negative test result doesn’t match reality — the test is negative but the person is actually sick.

Folks get confused with these often, so here’s a useful heuristic: positive vs. negative reflects the test outcome; true vs. false reflects whether the test got it right or got it wrong.

It’s a nice read.  The next step, after understanding these, is figuring out in which circumstances we want to weigh some of these measures more than others.

Related Posts

Data Science And Data Engineering In HDP 3.0

Saumitra Buragohain, et al, show off some of the things added to the Hortonworks Data Platform for data scientists and data engineers: We leverage the power of HDP 3.0 from efficient storage (erasure coding), GPU pooling to containerized TensorFlow and Zeppelin to enable this use case. We will the save the details for a different […]

Read More

Multi-Threaded R With Microsoft R Client

David Parr shows us how to get started with Microsoft R Client and performs some quick benchmarking: This message will pop up, and it’s worth noting as it’s got some information in it that you might need to think about: It’s worth noting that right now Microsoft r Client is lagging behind the current R version, and […]

Read More

Categories

March 2018
MTWTFSS
« Feb Apr »
 1234
567891011
12131415161718
19202122232425
262728293031