November 2015
Intermediate to advanced
190 pages
4h 11m
English
Content preview from Test Driven Machine LearningBecome an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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Starting from scratch
To begin with, let's start building an extremely simplistic bandit algorithm. The actual bandit class will have two methods: choose_treatment and log_payout. The first method will recommend the best treatment to choose, and log_payout will be used to report back on how effective the recommended treatment was.
The simplest way to approach this algorithm from a test-driven perspective is to start with a single treatment so that the algorithm only has one thing to recommend. The code for this test looks like the following:
from nose.tools import assert_equal import simple_bandit def given_a_single_treatment_test(): bandit = simple_bandit.SimpleBandit(['A']) chosen_treatment = bandit.choose_treatment() assert_equal(chosen_treatment, ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.