Wir beraten Sie gerne!
+49 40 253346-10     Kontakt
> > > 0A079G Detaillierte Beschreibung

Introduction to Machine Learning Models Using IBM SPSS Modeler (V18.2) (0A079G)

Detaillierter Kursinhalt

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

 

Cookies verbessern unsere Services. Durch die Benutzung unserer Website erklären Sie sich mit unserer Verwendung von Cookies einverstanden.   Verstanden.