Tree-Based Machine Learning Methods in SAS(R) Viya(R)

Kód kurzu: VBBF35

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Decision trees and tree-based ensembles are supervised learning models used for problems involving classification and regression. This course covers everything from using a single tree to more advanced bagging and boosting ensemble methods in SAS Viya. The course includes discussions of tree-structured predictive models and the methodology for growing, pruning, and assessing decision trees, forest and gradient boosting models. The course also explains isolation forest (an unsupervised learning algorithm for anomaly detection), deep forest (an alternative for neural network deep learning), and Poisson and Tweedy gradient boosted regression trees. In addition, many of the auxiliary uses of trees, such as exploratory data analysis, dimension reduction, and missing value imputation, are examined, and running open source in SAS and running SAS in open source are demonstrated.

The self-study e-learning includes:

  • Annotatable course notes in PDF format.
  • Virtual lab time to practice.

Odborní
certifikovaní lektori

Mezinárodne
uznávané certifikácie

Široká ponuka technických
a soft skills kurzov

Skvelý zákaznicky
servis

Prispôsobenie kurzov
presne na mieru

Termíny kurzov

Počiatočný dátum: Na vyžiadanie

Forma: E-learning

Dĺžka kurzu: 21 hodín

Jazyk: en

Cena bez DPH: 1 080 EUR

Registrovať

Počiatočný dátum: Na vyžiadanie

Forma: Na vyžiadanie

Dĺžka kurzu: 21 hodín

Jazyk: en

Cena bez DPH: 1 800 EUR

Registrovať

Počiatočný
dátum
Miesto
konania
Forma Dĺžka
kurzu
Jazyk Cena bez DPH
Na vyžiadanie E-learning 21 hodín en 1 080 EUR Registrovať
Na vyžiadanie Na vyžiadanie 21 hodín en 1 800 EUR Registrovať
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Kontakt

Cieľová skupina

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Predictive modelers and data analysts who want to build decision trees and ensembles of decision trees using SAS Visual Data Mining and Machine Learning in SAS Viya

Štruktúra kurzu

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Introduction to Decision Trees

  • Tree-structured models.
  • Recursive partitioning.

Growing a Decision Tree

  • Split search.
  • Splitting criteria.
  • Missing values and variable importance.

Preventing Overfitting in Decision Trees

  • Pruning.
  • Subtree methods.
  • Assessing decision trees.

Ensembles of Trees: Bagging, Boosting, and Forest

  • Ensembling.
  • Bagging.
  • Forest models.
  • Tree splitting in forests.
  • Hyperparameter tuning.
  • Model interpretability.

Tree-Based Gradient Boosting Machines

  • Boosting.
  • Gradient boosting.
  • Tree splitting in gradient boosting.
  • Early stopping.
  • Hyperparameter tuning.
  • Model interpretability.

A Practice Case Study

  • Data exploration.
  • Class levels consolidation.
  • Variable selection/dimension reduction.
  • Imputation.
  • Prediction profiling.

Predpokladané znalosti

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Before attending this course, you should have the following:
  • An understanding of basic statistical concepts. You can gain this knowledge from the SAS Visual Statistics in SAS Viya: Interactive Model Building course.
  • Familiarity with SAS Visual Data Mining and Machine Learning software. You can gain this knowledge from the Machine Learning Using SAS Viya course.
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