Teradata Vantage™ is our flagship analytic platform offering, which evolved from our industryleading Teradata® Database. Until references in content are updated to reflect this change, the term Teradata Database is synonymous with Teradata Vantage.
Teradata Warehouse Miner (TWM) is a set of Microsoft .NET interfaces and a multitier user interface that together help you understand the quality of data residing in a Teradata system, create analytic data sets, and build and score analytic models directly in the Teradata NewSQL Engine.
Teradata Warehouse Miner Feature  Machine Learning Engine Feature 

Data Explorer  Univariate Statistics, Histogram 
Frequency  Univariate Statistics, Approximate Cardinality 
Histogram  Histogram 
Statistics  Univariate Statistics 
Values  Univariate Statistics 
Overlap  [No counterpart] 
Text Field Analysis  [No counterpart] 
Adaptive Histogram  [No counterpart] 
Teradata Warehouse Miner Feature  Machine Learning Engine Feature 

Design Code  GLML1L2 (partial equivalence) 
Null Replacement  Interpolator (partial equivalence) 
Rescale  Scale Functions 
Type Casting  ConvertToCategorical 
Zscore  Scale Functions (using ScaleMethod “ustd") GLM1 CoxPH1 
Bin Code  [No counterpart] 
Derive  [No counterpart] 
Recode  [No counterpart] 
Sigmoid  [No counterpart] 
1Outputs a Zscore for each predictor 
Teradata Warehouse Miner Feature  Machine Learning Engine Feature 

Fast KMeans Clustering and Scoring  KMeans 
Gaussian Clustering and Scoring  GMM 
Gain Ratio and GiniIndex Decision Tree and Scoring  Decision Trees 
Decision Tree Scoring  Single_Tree_Predict 
CHAID Trees  Decision Trees 
Regression Trees  Decision Forests 
Linear Regression and Scoring  Linear Regression and LinReg Predict LAR Functions (variant of linear regression) GLM GLML1L2 
Matrix Building (Correlation, Covariance, etc.)  Correlation 
Factor Analysis: Principal Components Analysis (PCA)  Principal Components Analysis (PCA) 
Factor Scoring  PCAScore 
Logistic Regression  GLM GLML1L2 
Logistic Scoring  GLMPredict GLML1L2Predict 
Association Rules  BasketGenerator CFilter, FPGrowth and IdentityMatch FellegiSunter Recommender functions 
Stepwise Logistic Regression  GLM 
Poisson Clustering and Scoring  [No counterpart] 
Factor Analysis: Common Factors, Principal Access Factors, Rotations  [No counterparts] 
Association Sequence Analysis  [No counterpart] 
Collinearity Diagnostics  [No counterpart] 
Teradata Warehouse Miner Feature  Machine Learning Engine Feature 

Kolmogorov/Smirnov Test: One Sample  Distribution Matching 
Chi Square Test  GLM1: Pearson’s Chi Square Test, Wald Chi Square Test CoxPH1: Score Test, Wald Chi Square Test, and Likelihood Ratio Test DistributionMatchReduce: Pearson’s Chi Square Test2 
Kolmogorov/Smirnov Tests: Lilliefors, ShapiroWilk, D'Agostino and Pearson, Smirnov  [No counterparts] 
Parametric Tests: Two Sample TTest for Equal Means, FTest/NWay, FTest/Analysis of Variance (Two Way Unequal Sample Size)  [No counterparts] 
Rank Tests: ManWhitney/KruskalWallis, Wilcoxon Signed Ranks, Friedman Test with Kendall’s Coefficient of Concordance & Spearman’s Rho  [No counterparts] 
Median Test (based on contingency)  [No counterpart] 
Binomial Tests: Ztest and Sign Test  [No counterparts] 

Teradata Warehouse Miner Feature  Machine Learning Engine Feature 

Standard graphs of descriptive stats and algorithms  Visualizer (generates JSON description, does not render the graph) 
Decision Tree Graph  Visualizer 
Association Graph  Visualizer 
Data Explorer Thumbnail Graph  [No counterpart] 
Export to files  [No counterpart] 
Drilldown for most graphs  [No counterpart] 
TWM App Features
No corresponding features.
TWM Model Manager
No corresponding features.