Newly generated SQL plans will simply be included in SQL plan baselines only if it is found that the query performance will not become degraded, ideally optimizing performance. This means that the optimizer uses costing mechanisms to evaluate plans. With Manual SQL Management, plans can be fixed in baselines, thereby forcing the optimizer to consider only those plans from a plan baseline which have been manually marked as possible candidates. There may be reasons why a guarantee may be desired for the execution plans of certain SQL not to change, i.
Tools to relieve gruntwork, allowing the analyst to focus on the creative aspects of model development. Regression, Classification, and Logistic Regression enhanced to support massive datasets. New features for our core tools, based on user feedback and advances in data science.
Bridging-the-gap between the leading edge academic thinking of Jerome Friedman and Leo Breiman and real-world applications.
We call them "Automates". These "Automates" or experiments create multiple models automatically so that the analyst can easily see choices.
Banking Applications Automate Shaving Automate Shaving helps to identify subsets of informative data within large datasets containing correlated variables within the account data.
With automation, you may accomplish significant model reduction with minimal if any sacrifice to model accuracy. For example, start with a complete list of variables, and run automated shaving from the top to eliminate variables that look promising on the learn sample but fail to generalize.
Later you can run shaving from the bottom to automatically eliminate a major bulk of redundant and unnecessary predictors. Then follow up with "shaving error" to quickly zero in on the most informative subset of features.
As opposed to typical data mining tools, Automate Shaving offers more than the typical variable importance list. Expert modelers typically devote a lot of time and effort to optimizing their variable importance list; Automate Shaving automates this process. Fraud Detection Automate Priors In typical fraud detection applications the analyst is concerned with identifying different sets of rules leading to a varying probability of fraud.
Decision trees and TreeNet gradient boosting technology are typically used to build classification rules for detecting fraud. Any classification tree is constructed based on a specific user-supplied set of prior probabilities.
One set of priors will force trees to search for rules with high levels of fraud, while other sets of priors will produce trees with somewhat relaxed assumptions.
To gain the most benefits of tree-based rule searching approaches, analysts will try a large number of different configurations of prior probabilities.
This process is fully automated in Automate Priors.
The result is a large collection of rules ranging from extremely high confidence fraud segments with low support to moderate indication of fraud segments with very wide support.
Engineering Application Automate Target In a modern engineering application, as part of the experimental design, a large collection of sampled points may be gathered under different operating conditions.
It can be challenging to identify mutual dependencies among the different parameters. Automate Target gives you powerful means to automatically explore and extract all mutual dependencies among predictors. By the word "dependencies," we mean a potentially nonlinear multivariate relationship that goes way beyond the simplicity of conventional correlations.How to Write a Narrative Essay.
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