Python Machine Learning

Index

• A
• accuracy, computing of, 168–171
• algorithms
  • categories of in ML, 5
  • comparing ML algorithms, 258–260
  • evaluating ML algorithms, 260–261, 277–279
  • supervised learning algorithms, 5
  • Two‐Class Decision Jungle algorithm, 258, 259, 260
  • Two‐Class Logistic Regression algorithm, 258, 259, 260
  • Two‐Class Support Vector Machine algorithm, 258
  • unsupervised learning algorithms, 5, 7
• Anaconda, 88–1
• apply() function, 57, 58, 59
• area under the curve (AUC), 174
• argsort() function, 33
• arrange() function, 20
• array assignment, 34–38
• array indexing, 22–26
• array math, 27–34
• arrays
  • copying by reference, 34–35
  • copying by value (deep copy), 37
  • copying by view (shallow copy), 36–37
  • creating NumPy arrays, 20–21
  • reshaping of, 26–27
  • slicing of, 23–25
• asmatrix() function, 30
• auc() function, 174
• Auto MPG Data Set, 98
• Azure Machine Learning Studio (MAML)
  • comparing against other algorithms, 258–260
  • creating experiment, 248–252
  • evaluating machine learning algorithms, 260–261
  • example using Titanic experiment, 244–246
  • filtering data and making fields categorical, 252–254
  • introduction, 243
  • programmatically accessing web service, 263–266
  • publishing experiment, 261–263
  • publishing learning model as web service, 261–262
  • removing missing data, 254
  • splitting data for training and testing, 254–256
  • testing web service, 263
  • training a model, 256–258
  • uploading dataset, 247–248
  • use of, 246–266

• B
• Bagging, 143
• bar chart
  • defined, 73
  • plotting of, 73–77
• bar() function, 73
• bias, 141–144
• Boolean indexing, 22–23
• Boosting, ...