LIME

William Vorhies discusses a new technical paper on Local Interpretable Model-Agnostic Explanations:

What the model actually used for classification were these: ‘posting’, ‘host’, ‘NNTP’, ‘EDU’, ‘have’, ‘there’.  These are meaningless artifacts that appear in both the training and test sets and have nothing to do with the topic except that, for example, the word “posting” (part of the email header) appears in 21.6% of the examples in the training set but only two times in the class “Christianity.”

Is this model going to generalize?  Absolutely not.

An Example from Image Processing

In this example using Google’s Inception NN on arbitrary images the objective was to correctly classify “tree frogs”.  The classifier was correct in about 54% of cases but also interpreted the image as a pool table (7%) and a balloon (5%).

Looks like an interesting paper.  Click through for a link to the paper.

Related Posts

A Quick Keras Example

Shubham Dangare takes us through a quick example using Keras and TensorFlow in Python: Keras is a high-level neural networks API, written in Python and capable of running on top of Tensorflow, CNTK  or Theano. It was developed with a focus on enabling fast experimentation. In this blog, we are going to cover one small […]

Read More

ML Services and Injectable Code

Grant Fritchey looks at sp_execute_external_script for potential SQL injection vulnerabilities: The sharp eyed will see that the data set is defined by SQL. So, does that suffer from injection attacks? Short answer is no. If there was more than one result set within the Python code, it’s going to error out. So you’re protected there. […]

Read More

Categories

September 2016
MTWTFSS
« Aug Oct »
 1234
567891011
12131415161718
19202122232425
2627282930