SentenceExtractor Example - Teradata Vantage

Machine Learning Engine Analytic Function Reference

Product

Teradata Vantage

Release Number

8.00

1.0

Published

May 2019

Language

English (United States)

Last Update

2019-11-22

dita:mapPath

blj1506016597986.ditamap

dita:ditavalPath

blj1506016597986.ditaval

dita:id

B700-4003

lifecycle

Product Category

Teradata Vantage™

Input

Input table: paragraphs_input, which has paragraphs about common analytics topics (regression, decision Trees, and so on)

Input table: paragraphs_input
paraid	paratopic	paratext
1	Decision Trees	Decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. It is one of the predictive modelling approaches used in statistics, data mining and machine learning. Tree models where the target variable can take a finite set of values are called classification trees. In these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees.
2	Simple Regression	In statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. In other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible.
...	...	...

SQL Call

SELECT * FROM SentenceExtractor (
  ON paragraphs_input
  USING
  TextColumn ('paratext')
  Accumulate ('paraid', 'paratopic')
) AS dt ORDER BY 1, 3;

Output

paraid	paratopic	sentence_sn	sentence
1	Decision Trees	1	Decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value.
1	Decision Trees	2	It is one of the predictive modelling approaches used in statistics, data mining and machine learning.
1	Decision Trees	3	Tree models where the target variable can take a finite set of values are called classification trees.
1	Decision Trees	4	In these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels.
1	Decision	5	Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees.
2	Simple Regression	1	In statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable.
2	Simple Regression	2	In other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible.
3	Logistic Regression	1	Logistic regression was developed by statistician David Cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier).
3	Logistic Regression	2	The binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features).
3	Logistic Regression	3	As such it is not a classification method.
3	Logistic Regression	4	It could be called a qualitative response/discrete choice model in the terminology of economics.
4	Cluster analysis	1	Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters).
4	Cluster analysis	2	It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics.
4	Cluster analysis	3	Cluster analysis itself is not one specific algorithm, but the general task to solve.
4	Cluster analysis	4	It can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them.
5	Association rule learning	1	Association rule learning is a method for discovering interesting relations between variables in large databases.
5	Association rule learning	2	It is intended to identify strong rules discovered in databases using different measures of interestingness.
5	Association rule learning	3	Based on the concept of strong rules, Rakesh Agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (POS) systems in supermarkets.
5	Association rule learning	4	For example, the rule {onions, potatoes} => {burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat.