5.4.6 - Vantage Counterparts for Teradata Warehouse Miner Functions - Teradata Warehouse Miner

Vantage™ Counterparts for Teradata® Warehouse Miner Functions

prodname
Teradata Vantage
Teradata Warehouse Miner
vrm_release
1.1
5.4.6
-NA-
created_date
August 2019
category
Programming Reference
featnum
B700-4017-089K

Teradata Vantage™ is our flagship analytic platform offering, which evolved from our industry-leading 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 multi-tier 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.

The tables below show the mapping of TWM functions and features to their Teradata Vantage™ counterparts in the Teradata Machine Learning Engine. These counterparts provide similar functionality but not necessarily complete parity.
For information about all of the features and functions provided by TWM and Vantage, see Additional References.
Descriptive Statistics
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]
Transformations
Teradata Warehouse Miner Feature Machine Learning Engine Feature
Design Code GLML1L2 (partial equivalence)
Null Replacement Interpolator (partial equivalence)
Rescale Scale Functions
Type Casting ConvertToCategorical
Z-score Scale Functions (using ScaleMethod “ustd")

GLM1

CoxPH1

Bin Code [No counterpart]
Derive [No counterpart]
Recode [No counterpart]
Sigmoid [No counterpart]
1Outputs a Z-score for each predictor
Analytic Algorithms
Teradata Warehouse Miner Feature Machine Learning Engine Feature
Fast K-Means Clustering and Scoring KMeans
Gaussian Clustering and Scoring GMM
Gain Ratio and Gini-Index 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

Fellegi-Sunter

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]
Statistical Tests
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, Shapiro-Wilk, D'Agostino and Pearson, Smirnov [No counterparts]
Parametric Tests: Two Sample T-Test for Equal Means, FTest/NWay, FTest/Analysis of Variance (Two Way Unequal Sample Size) [No counterparts]
Rank Tests: Man-Whitney/Kruskal-Wallis, 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]
  • 1GLM and CoxPH perform various Chi square tests in order to find out if explainable variables are significant in a model.
  • 2Computes Pearson’s Chi square test (among others) to evaluate the fit of a distribution to the data.
Visualizations
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]
Drill-down for most graphs [No counterpart]

TWM App Features

No corresponding features.

TWM Model Manager

No corresponding features.