Formation IBM SPSS Modeler : Modèles avancés de Machine Learning (v18.2) Advanced Machine Learning Models Using IBM SPSS Modeler (V18.2)

Durée 1 jour
Niveau Avancé
Classe à distance

Vous pouvez suivre cette formation en direct depuis votre domicile ou votre lieu de travail. Plus d'informations sur notre solution de classe à distance...

Référence 0A039G
Éligible CPF Non
Cours officiel IBM

This course presents advanced models available in IBM SPSS Modeler. The participant is first introduced to a technique named PCA/Factor, to reduce the number of fields to a number of core factors, referred to as components or factors.

The next topics focus on supervised models, including Support Vector Machines, Random Trees, and XGBoost. Methods are reviewed on how to analyze text data, combine individual models into a single model, and how to enhance the power of IBM SPSS Modeler by adding external models, developed in Python or R, to the Modeling palette.

Public :

This training targets especificaly the data scientists, the business analysts, and eventually experienced users of IBM SPSS Modeler who want to learn about advanced techniques in the software.

Prérequis :

Knowledge of your business requirements.
Required : IBM SPSS Modeler Foundations (V18.2) course (0A069G/0E069G) or equivalent knowledge of how to import, explore, and prepare data with IBM SPSS Modeler v18.2, and know the basics of modeling.
Recommended : Introduction to Machine Learning Models Using IBM SPSS Modeler (V18.2) course (0A079G/0E079G), or equivalent knowledge or experience with the product about supervised machine learning models (CHAID, C&R Tree, Regression, Random Trees, Neural Net, XGBoost), unsupervised machine learning models (TwoStep Cluster), and association machine learning models such as APriori.

Introduction to advanced machine learning models

Taxonomy of models

Overview of supervised models

Overview of models to create natural groupings Group fields : Factor Analysis and Principal Component Analysis

Factor Analysis basics

Principal Components basics

Assumptions of Factor Analysis

Key issues in Factor Analysis

Improve the interpretability

Factor and component scores Predict targets with Nearest Neighbor Analysis

Nearest Neighbor Analysis basics

Key issues in Nearest Neighbor Analysis

Assess model fit Explore advanced supervised models

Support Vector Machines basics

Random Trees basics

XGBoost basics

Introduction to Generalized Linear Models

Generalized Linear Models

Available distributions

Available link functions

Combine supervised models

Combine models with the Ensemble node

Identify ensemble methods for categorical targets

Identify ensemble methods for flag targets

Identify ensemble methods for continuous targets

Meta-level modeling Use external machine learning models

IBM SPSS Modeler Extension nodes

Use external machine learning programs in IBM SPSS Modeler Analyze text data

Text Mining and Data Science

Text Mining applications

Modeling with text data

Date de mise à jour du programme : 21/02/2024

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