Category Archives: machine learning

What have I been writing?

Just because I missed posting for the last year, doesn’t mean I have not been writing. Perhaps I have been writing more. Here is something that I just wrote for a perspective on opportunities for machine learning in population health.

Machine learning (ML) is emerging as a technology, climbing the “peak of inflated expectations” or perhaps even starting to slip into the “trough of disillusionment”, in the terms of the technology hype cycle,[ref] and offers both opportunities and threats to population health. ML is a technique for constructing computer algorithms, and what distinguishes ML methods from other computer solutions is that, while the structure of the computer program may be fixed, the details are learned from data. This data-driven approach is now dominant in Artificial Intelligence (AI), especially through deep neural networks, and stands in contrast to the old way, an expert-algorithms approach in which rules summarizing expert knowledge were painstakingly constructed by engineers and domain specialists. ML has succeeded by trading experts and programmers for data and nonparametric statistical models. However, the applications where ML has been successfully deployed remain limited. AI luminary Andrew Ng provides this concise heuristic: “[i]f a typical person can do a mental task with less than one second of thought, we can probably automate it using AI either now or in the near future.”[ref]

The editor only wants 1,000 words, so this is getting cut.

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Filed under machine learning, Uncategorized

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:

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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: [tag: my-research]

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Why do I call that variable `clf`?

From the sklearn docs: “We call our estimator instance `clf`, as it is a classifier.”

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Intro to SkFlow

This could be a useful guide:

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The mysterious non-mystery of boosting


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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? …

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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(
        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.

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Filed under machine learning

To read: EnsembleMatrix paper

EnsembleMatrix: Interactive Visualization to Support Machine Learning with Multiple Classifiers

I want one

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Filed under dataviz, machine learning

Brief survey on sequence classification

hi Abie,

It was great speaking with you. This is the paper I was talking about.

Looking forward to know more about each other’s work.



Filed under disease modeling, machine learning