Evaluating A Data Science Project

Tom Fawcett gives us an interesting evaluation of a data science case study:

The model is a fully connected neural network with three hidden layers, with a ReLU as the activation function. They state that data from Google Compute Engine was used to train the model (implemented in TensorFlow), and Cloud Machine Learning Engine’s HyperTune feature was used to tune hyperparameters.

I have no reason to doubt their representation choices or network design, but one thing looks odd. Their output is two ReLU (rectifier) units, each emitting the network’s accuracy (technically: recall) on that class. I would’ve chosen a single Softmax unit representing the probability of Large Loss driver, from which I could get a ROC or Precision-Recall curve. I could then threshold the output to get any achievable performance on the curve. (I explain the advantages of scoring over hard classification in this post.)

But I’m not a neural network expert, and the purpose here isn’t to critique their network design, just their general approach. I assume they experimented and are reporting the best performance they found.

Read the whole thing.

Related Posts

Markov Chains In Python

Sandipan Dey shows off various uses of Markov chains as well as how to create one in Python: Perspective. In the 1948 landmark paper A Mathematical Theory of Communication, Claude Shannon founded the field of information theory and revolutionized the telecommunications industry, laying the groundwork for today’s Information Age. In this paper, Shannon proposed using a Markov chain to […]

Read More

More DBA Salary Research

Ginger Grant digs into the DBA salary survey a bit further: I know that I have heard that if you want to make money you need to get into management. Being a good manager is not the same skill set as being a good database professional, and there are many people who do not want to […]

Read More

Categories

August 2017
MTWTFSS
« Jul Sep »
 123456
78910111213
14151617181920
21222324252627
28293031