Index
A
- Aikake's Information Criterion (AIC) / Modeling and evaluation
- algorithm flowchart
- American Diabetes Association (ADA)
- apriori algorithms
- Area Under the Curve (AUC)
- Artificial Neural Networks (ANNs)
- arules* Mining Association Rules and Frequent Itemsets
- Augmented Dickey-Fuller (ADF) test
- Autocorrelation Function (ACF)
- Autoregressive Integrated Moving Average (ARIMA) models
B
C
- Carbon Dioxide Information Analysis Center (CDIAC)
- caret package
- classification methods
- classification models
- classification trees
- cluster analysis
- Cohen's Kappa statistic
- collaborative filtering
- Cook's distance / Business understanding
- Corpus
- Cosine Similarity
- CRISP-DM process
- Cross-Entropy
- cross-validation
- Cross Correlation Function (CCF)
- CRUTEM4 surface air temperature
D
- data frame
- data preparation process
- data understanding process
- deep learning
- deployment process / Deployment
- dirichlet distribution
- Discriminant Analysis (DA)
- Document-Term Matrix (DTM)
- dynamic topic modelling
E
- ECLAT algorithms
- eigenvalues
- eigenvectors
- elastic net
- equimax
- Euclidian Distance
- evaluation process
- exponential smoothing models
- Extract, Transport, and Load (ETL)
F
- F-Measure
- False Positive Rate (FPR)
- Feed Forward network
- Final Prediction Error (FPE)
- Fine Needle Aspiration (FNA)
- first principal component
- forward stepwise selection / Modeling and evaluation
G
- Gedeon Method
- glmnet package
- Gower
- gradient boosted trees
- gradient boosting
- gradient boosting classification
- gradient boosting regression
- Granger causality
- Graphical User Interface (GUI)
H
I
- Integrated Development Environment (IDE)
- interquartile range
- item-based collaborative filtering (IBCF)
K
- K-fold cross-validation
- k-means clustering
- K-Nearest Neighbors (KNN)
- K-sets
- kernel trick
- KNN modeling
L
- L1-norm
- L2-norm
- LASSO
- Latent Dirichlet Allocation (LDA)
- lazy learning
- Leave-One-Out-Cross-Validation (LOOCV)
- Leave-One-Out Cross-Validation (LOOCV) / Modeling and evaluation
- Linear Discriminant Analysis (QDA)
- linear model considerations
- linear regression
- linear regression model
- logistic regression
- logistic regression model
- loss function
M
- Mallow's Cp (Cp) / Modeling and evaluation
- margin
- market basket analysis
- matrices
- mean squared error (MSE)
- medoid
- modeling process
- multivariate linear regression
N
O
P
- 2p models
- Partial Autocorrelation Function (PACF)
- Partitioning Around Medoids (PAM)
- Pearson Correlation Coefficient
- Polarity
- Porter stemming algorithm
- Prediction Error Sum of Squares (PRESS) / Modeling and evaluation
- principal components
- principal components analysis (PCA)
- Principal Components Analysis (PCA)
Q
R
- R
- radical
- random forest
- random forest classification
- random forest regression
- Receiver Operating Characteristic (ROC)
- Receiver Operating Characteristic Curves (ROC)
- recommendation engine
- overview / An overview of a recommendation engine
- collaborative filtering / An overview of a recommendation engine
- business understanding / Business understanding and recommendations
- data, understanding / Data understanding, preparation, and recommendations
- data, preparing / Data understanding, preparation, and recommendations
- modeling / Modeling, evaluation, and recommendations
- evaluation / Modeling, evaluation, and recommendations
- recommendations / Modeling, evaluation, and recommendations
- recommenderlab library
- regression trees
- regularization
- regularization, modeling
- Residual Sum of Squares (RSS) / Univariate linear regression
- Residuals vs Leverage plot / Business understanding
- Restricted Boltzmann Machine
- ridge regression
- Root Mean Square Error (RMSE)
- R packages
- RStudio
S
- Schwarz-Bayes Criterion (SC)
- second principal component
- shrinkage penalty
- singular value decomposition (SVD)
- slack variables
- Sparse Coding Model
- summary stats
- Sum of Squared Error
- sum of squared error (SSE)
- Support Vector Machines (SVM)
- suspected outliers
- SVM modeling
T
- Term-Document Matrix (TDM)
- text mining
- topic models
- tree-based learning
- True Positive Rate (TPR)
U
- univariate linear regression
- univariate time series
- user-based collaborative filtering (UBCF)
V
W
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