Index
A
B
- backpropagation algorithm
- backward elimination
- backward selection
- bagging
- bagging, for binary classification
- Banknote Authentication data set
- batch machine learning model / Real-time and batch machine learning models
- Baum-Welch algorithm
- Bayesian Information Criterion (BIC)
- Bayesian networks
- Bayesian probability
- Bayes Theorem
- bias
- Big Data
- binary classification models
- biological neuron
- boosting
- bootstrapped samples
- bootstrapping
- bootstrap resampling
- bootstrap sampling
- Box-Cox transformation / Feature transformations
- Brownian Motion
C
- C5.0 algorithm
- caret package
- CART classification trees
- CART methodology
- CART regression trees / CART regression trees
- categorical features
- characteristic polynomial
- chemical biodegradation
- CHI squared
- classification metrics
- classification model / Regression and classification models
- classification models
- class membership
- clustering
- coefficients
- collaborative filtering
- complex skill learning
- complex skills
- conditional independence
- confidence interval
- confusion matrix
- Correlated Topic Model (CTM)
- correlogram
- cost-complexity tuning
- cost function
- CPU performance
- credit scores
- cross-validation
- cycle
D
- data, pre-processing
- data, recommendation systems
- data columns
- data set
- data sets
- decision tree models
- decision trees
- dendrites
- deviance
- dimensionality reduction / Feature engineering and dimensionality reduction
- directed acyclic graph (DAG)
- directed graph
- Dirichlet distribution / The Dirichlet distribution
- Discrete AdaBoost
- discrete white noise
- document term matrix
- dynamic programming
E
F
G
H
- handwritten digits
- HMM
- hyperplanes
I
- ID3
- Independence of Irrelevant Alternatives (IIA)
- independent and identically distributed (iid)
- information statistic
- inhibitors
- inner products / Inner products
- intense earthquakes
- intercept
- interquartile range
- invertible
- item-based collaborative filtering
J
K
L
M
- M5
- magic
- MAGIC Gamma Telescope data set
- margin / Margins and out-of-bag observations
- Markov Chain Monte Carlo (MCMC)
- matrix, recommendation systems
- Matrix Market format
- maximal margin classification
- maximal margin hyperplane
- maximum likelihood estimation / Maximum likelihood estimation
- McCulloch-Pitts model of a neuron
- mean
- mean average error (MAE) / Evaluating individual predictions
- mean function
- mean squared error (MSE) / Evaluating individual predictions
- Mean Square Error (MSE)
- mean square error (MSE)
- median
- Missing At Random (MAR)
- Missing Completely At Random (MCAR)
- missing data / Missing data
- Missing Not At Random (MNAR)
- missing values
- mixed selection
- MLP network
- model
- model, deploying
- model deviance / Model deviance
- model parameters
- models
- model types
- about / Types of models
- supervised model / Supervised, unsupervised, semi-supervised, and reinforcement learning models
- unsupervised model / Supervised, unsupervised, semi-supervised, and reinforcement learning models
- semi-supervised model / Supervised, unsupervised, semi-supervised, and reinforcement learning models
- reinforcement learning model / Supervised, unsupervised, semi-supervised, and reinforcement learning models
- parametric model / Parametric and nonparametric models
- nonparametric model / Parametric and nonparametric models
- regression model / Regression and classification models
- classification model / Regression and classification models
- real-time machine learning model / Real-time and batch machine learning models
- batch machine learning model / Real-time and batch machine learning models
- moving average (MA)
- moving average models / Moving average models
- multi-class classification, with support vector machines
- multinom()function
- multinomial logistic regression
- multiple linear regression
N
O
P
- p-value
- parametric model / Parametric and nonparametric models
- partial autocorrelation function (PACF)
- Perception Action Cycles (PACs)
- perceptron algorithm
- performance metrics
- performance metrics, for linear regression / Performance metrics for linear regression
- pocket perceptron algorithm
- polynomial kernel
- Porter Stemmer
- post-pruning
- Precision
- predictive modeling
- price, of used cars
- Principal Component Analysis (PCA)
- Principles and Practice of Knowledge Discovery in Databases
- probabilistic graphical models
- probability
- promoter gene sequences
- proportional odds
- pruning
Q
- Q-Q plots
- QSAR biodegradation
- Quantile-Quantile plot (Q-Q plot)
R
S
T
- tests, for linear regression
- test set
- time-domain methods
- time series
- time series models
- topic modeling
- topics, of online news stories
- total sum of squares (TSS)
- training set
- tree models
- tree pruning / Tree pruning
- trend
- true negatives
- true positives
- true sum of squares (TSS)
- Type I error
- Type II error
U
V
- Variational Expectation Maximization (VEM)
- Viterbi algorithm
W
- wavelet transform
- weight
- white noise time series
- wine quality
- word cloud
- word distributions / Word distributions
Z
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