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.
| 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 |
| 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 | |
| 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] |
| 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] |
|
|
| 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.