Index
A
B
C
D
E
F
G
H
I
J
K
L
M
- machine learning
- machine learning algorithms
- magrittr package
- Manhattan distance / Measuring similarity with distance
- MapReduce
- marginal likelihood / Computing conditional probability with Bayes' theorem
- market basket analysis / Types of machine learning algorithms
- massive matrices
- matrix
- matrix format data / Types of input data
- matrix inverse / Multiple linear regression
- 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
- median / Measuring the central tendency – mean and median
- medical expenses, predicting with linear regression
- message passing interface (MPI)
- meta-learners / Types of machine learning algorithms
- meta-learning
- microarray / Analyzing bioinformatics data
- Microsoft Azure / Step 5 – improving model performance
- Microsoft Excel files
- min-max normalization / Preparing data for use with k-NN
- mobile phone filtering, with Naive Bayes algorithm
- about / Example – filtering mobile phone spam with the Naive Bayes algorithm
- data collection / Step 1 – collecting data
- data exploration / Step 2 – exploring and preparing the data
- data preparation / 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 dataset, creating / Data preparation – creating training and test datasets
- test dataset, 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 trees
- multicore package
- multilayer network / The number of layers
- multilayer perceptron (MLP) / The direction of information travel
- multimodal / Measuring the central tendency – the mode
- multinomial logistic regression
- multiple linear regression
- multiple regression
- multivariate relationships / Exploring relationships between variables
- mutually exclusive event / Understanding probability
N
O
P
Q
R
- 1R algorithm
- R
- radial basis function (RBF) / Activation functions
- random-access memory (RAM) / Data storage
- random forest models
- random forest performance
- random forests / Random forests
- random sample / Data preparation – creating random training and test datasets
- range / Measuring spread – quartiles and the five-number summary
- ranger
- RCurl package
- readr package
- real-world data
- recall / Precision and recall
- receiver operating characteristic (ROC) curve / Visualizing performance tradeoffs with ROC curves
- rectifier / Step 5 – improving model performance
- rectifier linear unit (ReLU) / Step 5 – improving model performance
- recurrent network / The direction of information travel
- recursive partitioning
- regression
- regression analysis / Understanding regression
- regression trees
- reinforcement learning / Types of machine learning algorithms
- relationships
- repeated holdout / The holdout method
- repeated k-fold CV / Cross-validation
- residuals / Ordinary least squares estimation
- resubstitution error / Estimating future performance
- RHadoop project
- rio package
- Microsoft Excel files, importing / Importing Microsoft Excel, SAS, SPSS, and Stata files with rio
- SAS files, importing / Importing Microsoft Excel, SAS, SPSS, and Stata files with rio
- SPSS files, importing / Importing Microsoft Excel, SAS, SPSS, and Stata files with rio
- Stata files, importing / Importing Microsoft Excel, SAS, SPSS, and Stata files with rio
- reference / Importing Microsoft Excel, SAS, SPSS, and Stata files with rio
- RIPPER algorithm
- ROC curves
- root node
- rote learning / Why is the k-NN algorithm lazy?
- R packages
- R performance, improving
- RStudio
- rule learner / What makes trees and rules greedy?
- rules
- RWeka / Installing R packages
S
T
U
V
W
X
- xml2 homepage
- XML documents
- XML package
Z
..................Content has been hidden....................
You can't read the all page of ebook, please click
here login for view all page.