Index
A
B
C
D
E
F
G
- gain ratio
- Gaussian Radial Basis Function (RBF) kernel / Using kernels for non-linear spaces
- generalization
- generalized linear models (GLM)
- Gini index
- GPU computing
- gputools package
- gradient descent
- graph data
- greedy learners
- grid
H
I
J
K
L
M
- M5' algorithm (M5-prime) / Step 5 – improving model performance
- machine learning
- machine learning algorithm
- machine learning algorithms
- Manhattan distance
- MapReduce programming model
- 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 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, Taking subsets of association rules
- association rules, saving to file / Saving association rules to a file or data frame
- massive matrices
- matrix
- matrix() function / Matrixes and arrays
- Maximum Margin Hyperplane (MMH)
- mcapply() function / Using a multitasking operating system with multicore
- mean
- mean() function / Measuring the central tendency – mean and median, Ordinary least squares estimation
- mean absolute error (MAE) / Measuring performance with mean absolute error
- median
- median() function / Measuring the central tendency – mean and median
- 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, exploring among features / 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, Transformation – converting a numeric variable to a binary indicator, Putting it all together – an improved regression model
- meta-learning methods
- Microsoft Excel / Importing and saving data from CSV files
- Microsoft Excel spreadsheets
- Microsoft SQL
- min-max normalization
- Mobile Phone Spam
- Mobile Phone Spam 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
- text data, processing for analysis / Data preparation – processing text data for analysis
- 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
- mode
- mode() function / Measuring the central tendency – the mode
- model
- model performance
- model performance, breast cancer example
- model trees
- multicore package
- multidimensional feature space / The kNN algorithm
- multilayer network
- Multilayer Perceptron (MLP)
- multimodal / Measuring the central tendency – the mode
- multiple linear regression
- multiple workstations
- multitasking operating system
- multivariate relationships
- MySpace / Finding teen market segments using k-means clustering
- MySQL
N
O
P
Q
R
S
T
U
V
W
- web
- weighted voting process
- wine quality estimation, with regression trees
- word cloud
X
- xlsx package
- XML
- XML package
Z
- z-score standardization
- ZeroR
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