Index
A
B
C
D
E
F
G
H
I
J
K
L
M
- machine learning
- machine learning, in practice
- machine learning, process
- machine learning algorithms
- magrittr package
- MapReduce
- marginal likelihood
- market basket analysis example
- data, collecting / Step 1 – collecting data
- data, preparing / Step 2 – exploring and preparing the data
- data, exploring / Step 2 – exploring and preparing the data
- sparse matrix, creating for transaction data / Data preparation – creating a sparse matrix for transaction data
- item support, visualizing / Visualizing item support – item frequency plots
- transaction data, visualizing / Visualizing the transaction data – plotting the sparse matrix
- model, training on data / Step 3 – training a model on the data
- model performance, evaluating / Step 4 – evaluating model performance
- model performance, improving / Step 5 – improving model performance
- set of association rules, sorting / Sorting the set of association rules
- subset of association rules, sorting / Taking subsets of association rules
- association rules, saving to file / Saving association rules to a file or data frame
- association rules, saving to data frame / Saving association rules to a file or data frame
- matrix / Matrixes and arrays
- matrix notation / Multiple linear regression
- maximum margin hyperplane (MMH) / Classification with hyperplanes
- mean / Measuring the central tendency – mean and median
- mean absolute error (MAE) / Measuring performance with the mean absolute error
- medical expenses, predicting with linear regression
- about / Example – predicting medical expenses using linear regression
- data, collecting / Step 1 – collecting data
- data, preparing / Step 2 – exploring and preparing the data
- data, exploring / Step 2 – exploring and preparing the data
- correlation matrix / Exploring relationships among features – the correlation matrix
- relationships, visualizing among features / Visualizing relationships among features – the scatterplot matrix
- scatterplot matrix / Visualizing relationships among features – the scatterplot matrix
- model, training on data / Step 3 – training a model on the data
- model performance, training / Step 4 – evaluating model performance
- model performance, improving / Step 5 – improving model performance, Model specification – adding non-linear relationships, Transformation – converting a numeric variable to a binary indicator, Model specification – adding interaction effects, Putting it all together – an improved regression model
- message-passing interface (MPI)
- meta-learners / Types of machine learning algorithms
- meta-learning methods
- min-max normalization / Preparing data for use with k-NN
- mobile phone spam
- mobile phone spam example
- data, collecting / Step 1 – collecting data
- dat a collecting, URL / Step 1 – collecting data
- data, preparing / Step 2 – exploring and preparing the data
- data, exploring / Step 2 – exploring and preparing the data
- text data, cleaning / Data preparation – cleaning and standardizing text data
- text data, standardizing / Data preparation – cleaning and standardizing text data
- text documents, splitting into words / Data preparation – splitting text documents into words
- training, creating / Data preparation – creating training and test datasets
- test datasets, creating / Data preparation – creating training and test datasets
- text data, visualizing / Visualizing text data – word clouds
- indicator features, creating for frequent words / Data preparation – creating indicator features for frequent words
- model, training on data / Step 3 – training a model on the data
- model performance, evaluating / Step 4 – evaluating model performance
- model performance, improving / Step 5 – improving model performance
- model performance
- model performance, breast cancer example
- model trees / Understanding regression trees and model trees
- multicore package
- multilayer network
- Multilayer Perceptron (MLP)
- multimodal / Measuring the central tendency – the mode
- multinomial logistic regression / Understanding regression
- multiple linear regression / Understanding regression
- multiple R-squared value (coefficient of determination) / Step 4 – evaluating model performance
- multivariate relationships
N
O
P
- parallel cloud computing
- parallel computing
- parameter tuning
- pattern discovery / Types of machine learning algorithms
- Pearson's correlation coefficient / Correlations
- performance
- performance measures
- performance tradeoffs
- poisonous mushrooms
- poisonous mushrooms example, with rule learners
- Poisson regression
- polynomial kernel / Using kernels for non-linear spaces
- positive predictive value / Precision and recall
- posterior probability
- postpruning
- pre-pruning
- precision / Precision and recall
- predictive model / Types of machine learning algorithms
- prior probability
- probability
- proprietary files
- about / Working with proprietary files and databases
- Microsoft Excel files, reading / Reading from and writing to Microsoft Excel, SAS, SPSS, and Stata files
- Microsoft Excel files, writing / Reading from and writing to Microsoft Excel, SAS, SPSS, and Stata files
- SAS files, writing / Reading from and writing to Microsoft Excel, SAS, SPSS, and Stata files
- SAS files, reading / Reading from and writing to Microsoft Excel, SAS, SPSS, and Stata files
- SPSS files, reading / Reading from and writing to Microsoft Excel, SAS, SPSS, and Stata files
- SPSS files, writing / Reading from and writing to Microsoft Excel, SAS, SPSS, and Stata files
- Stata files, writing / Reading from and writing to Microsoft Excel, SAS, SPSS, and Stata files
- Stata files, reading / Reading from and writing to Microsoft Excel, SAS, SPSS, and Stata files
- proprietary microarray
- pure / Choosing the best split
- purity / Choosing the best split
Q
R
S
T
U
V
W
- web pages
- web scraping
- wine quality estimation, with regression trees
- word cloud
- wordcloud package
X
- xml2 GitHub
- XML documents
- XML package
Z
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