Résumé du cours
This course provides an introduction to supervised models, unsupervised models, and association models. This is an application-oriented course and examples include predicting whether customers cancel their subscription, predicting property values, segment customers based on usage, and market basket analysis.
A qui s'adresse cette formation
- Data scientists
- Business analysts
- Clients who want to learn about machine learning models
Pré-requis
- Knowledge of your business requirements
Contenu
Introduction to machine learning models
- • Taxonomy of machine learning models
- • Identify measurement levels
- • Taxonomy of supervised models
- • Build and apply models in IBM SPSS Modeler
Supervised models: Decision trees - CHAID
- • CHAID basics for categorical targets
- • Include categorical and continuous predictors
- • CHAID basics for continuous targets
- • Treatment of missing values
Supervised models: Decision trees - C&R Tree
- • C&R Tree basics for categorical targets
- • Include categorical and continuous predictors
- • C&R Tree basics for continuous targets
- • Treatment of missing values
Evaluation measures for supervised models
- • Evaluation measures for categorical targets
- • Evaluation measures for continuous targets
Supervised models: Statistical models for continuous targets - Linear regression
- • Linear regression basics
- • Include categorical predictors
- • Treatment of missing values
Supervised models: Statistical models for categorical targets - Logistic regression
- • Logistic regression basics
- • Include categorical predictors
- • Treatment of missing values
Supervised models: Black box models - Neural networks
- • Neural network basics
- • Include categorical and continuous predictors
- • Treatment of missing values
Supervised models: Black box models - Ensemble models
- • Ensemble models basics
- • Improve accuracy and generalizability by boosting and bagging
- • Ensemble the best models
Unsupervised models: K-Means and Kohonen
- • K-Means basics
- • Include categorical inputs in K-Means
- • Treatment of missing values in K-Means
- • Kohonen networks basics
- • Treatment of missing values in Kohonen
Unsupervised models: TwoStep and Anomaly detection
- • TwoStep basics
- • TwoStep assumptions
- • Find the best segmentation model automatically
- • Anomaly detection basics
- • Treatment of missing values
Association models: Apriori
- • Apriori basics
- • Evaluation measures
- • Treatment of missing values
Association models: Sequence detection
- • Sequence detection basics
- • Treatment of missing values
Preparing data for modeling
- • Examine the quality of the data
- • Select important predictors
- • Balance the data
Allemand
Anglais