Index

A

• advanced regression
  • about / Refresher on advanced regression
• anatomy, test
  • about / The anatomy of a test
  • given / Given
  • when / When
  • then / Then
• Area Under Curve score (AUC score) / Measuring model accuracy

B

• bandit
  • about / Understanding a bandit
• behavior-driven development (BDD)
  • about / Behavior-driven development
  • reference link, for article / Behavior-driven development
• bootstrapping bandit
  • about / A bootstrapping bandit

C

• classification
  • about / Classification overview
• classification models
  • quantifying / Quantifying the classification models
• classifier
  • wiring up / Beginning the development
  • upgrading / Upgrading the classifier
  • applying / Applying our classifier
• classifier chooser
  • creating / Creating a classifier chooser (it needs to run tests to evaluate classifier performance)
• classifiers
  • hooking up / Getting choosey
• clustering
  • about / Clustering
• complex example
  • generating / Generating a more complex example

D

• data
  • generating / Generating our own data, Generating data
• data, GitHub repo
  • reference link / Generating our own data
• decision trees
  • about / Decision trees

E

• exploitation
  • about / Understanding a bandit
• exploration
  • about / Understanding a bandit

F

• F-test / Quantifying model quality

G

• Gaussian Naïve Bayes classifier
  • using / Gaussian classification by hand

H

• highest level
  • starting at / Starting at the highest level

I

• improved models validation approaches
  • about / Different approaches to validating the improved models
  • classification overview / Classification overview
  • regression / Regression
• interfaces
  • about / Planning our journey

J

• Jarque-Bera test / Quantifying model quality

K

• Kaggle competition
  • reference link / Applying our classifier

L

• Likelihood Ratio p-value
  • about / Test driving our model
• Linear Regression
  • using / The real world
• logistic data
  • generating / Generating logistic data
• Logistic Regression
  • using / The real world

M

• machine learning
  • applying, to test-driven development (TDD) / TDD applied to machine learning
• model
  • foundation, building / Building the foundations of our model
  • cross-validating / Cross-validating our model
  • test driving / Test driving our model
• model accuracy
  • measuring / Measuring model accuracy
• model quality
  • quantifying / Quantifying model quality
• Monte Carlo methods
  • about / Testing with simulation
• multiple coefficient of determination / Quantifying model quality

P

• p-value / Quantifying model quality
• perceptron
  • about / Getting started
  • testing / Getting started

Q

• Q-Q Plot / Quantifying model quality

R

• Random Forest
  • upgrading to / Upgrading to Random Forest
• randomized probability matching bandit algorithm
  • about / A randomized probability matching algorithm
• randomness
  • dealing with / Dealing with randomness
• real world situations
  • simulating / Simulating real world situations
• regression
  • about / Regression
  • assumptions / Regression assumptions
• ROC curve
  • reference link / Quantifying the classification models
• RPMBandit
  • about / Multi-armed armed bandit throw down
  • versus SimpleBandit / Multi-armed armed bandit throw down

S

• sciKit-learn
  • reference link / Decision trees
• SimpleBandit
  • about / Multi-armed armed bandit throw down
  • versus RPMBandit / Multi-armed armed bandit throw down
• simplistic bandit algorithm
  • building / Starting from scratch
• simulation
  • testing with / Testing with simulation
• sklearn
  • about / The real world
  • URL, for documentation / The real world
• straight bootstrapping
  • problem / The problem with straight bootstrapping

T

• t-statistic / Quantifying model quality
• TDD
  • about / Test-driven design
  • reference link / Test-driven design
• test
  • about / Our first test
  • anatomy / The anatomy of a test
• test-driven development (TDD)
  • about / Test-driven development
  • requisites / Test-driven development
  • applying, to machine learning / TDD applied to machine learning
• test-driven development (TDD) cycle
  • about / The TDD cycle
  • Red / Red
  • Green / Green
  • Refactor / Refactor
• testable documentation
  • developing / Developing testable documentation