Category Archives: machine learning

Deep Learning Frameworks

I was nearly convinced that Google’s TensorFlow would take over the world, but now I’ll need to also consider MXNet: http://mxnet.io/

Comments Off on Deep Learning Frameworks

Filed under machine learning, software engineering

CCA for diverticulitis

A short paper from my work on predicting who will get elective surgery for diverticulitis is on arXiv: https://arxiv.org/abs/1612.00516 [tag: my-research]

Comments Off on CCA for diverticulitis

Filed under machine learning

Why do I call that variable `clf`?

From the sklearn docs: “We call our estimator instance `clf`, as it is a classifier.” http://scikit-learn.org/stable/tutorial/basic/tutorial.html#learning-and-predicting

Comments Off on Why do I call that variable `clf`?

Filed under machine learning, software engineering

Intro to SkFlow

This could be a useful guide: http://terrytangyuan.github.io/2016/03/14/scikit-flow-intro/

Comments Off on Intro to SkFlow

Filed under machine learning

The mysterious non-mystery of boosting

success_of_boosting

Comments Off on The mysterious non-mystery of boosting

March 9, 2016 · 8:00 am

Article I’m interested in: “Machine Learning and the Profession of Medicine”

Darcy AM, Louie AK, Roberts L. Machine Learning and the Profession of Medicine. JAMA. 2016;315(6):551-552. doi:10.1001/jama.2015.18421.

> Must a physician be human? …

http://jama.jamanetwork.com/article.aspx?articleID=2488315

Comments Off on Article I’m interested in: “Machine Learning and the Profession of Medicine”

Filed under machine learning

Using the sklearn text.CountVectorizer

I have been getting some great success from the scikits-learn CountVectorizer transformations. Here are some notes on how I like to use it:

import sklearn.feature_extraction

ngram_range = (1,2)

clf = sklearn.feature_extraction.text.CountVectorizer(
        ngram_range=ngram_range,
        min_df=10,  # minimum number of docs that must contain n-gram to include as a column
        #tokenizer=lambda x: [x_i.strip() for x_i in x.split()]  # keep '*' characters as tokens
    )

There is a stop_words parameter that is also sometimes useful.

Comments Off on Using the sklearn text.CountVectorizer

Filed under machine learning