Modeling FAQ
Frequently asked questions regarding the use of machine learning models in the G2M platform
51 articles
- What types of models can I build with the G2M platform?
- How do I create a new model?
- What variables should I select?
- Which algorithm should I pick?
- Which algorithm should I pick for my A/B testing analysis?
- Which algorithm should I pick for my marketing mix model?
- What is the estimated sample size?
- When should I downsample my dataset?
- What dataset training size should I use?
- What is model drift?
- How do I delete an existing model?
- How do I share a model with another user?
- What is the predict stage of a model?
- How can I make predictions using an existing model?
- How can I export predictions I just generated?
- How do I interpret error metrics for my marketing mix model?
- What is outlier removal?
- How can I export my training results to a presentation?
- How can I convert my G2M model to a Jupyter notebook?
- How can I export my prediction results to a spreadsheet?
- How can I duplicate a model?
- How is prediction performance monitored?
- What is the Infer stage of a model
- What is MARK?
- What is Hyper Tuning?
- Which algorithm should I pick for my propensity model?
- What is SMOTE preprocessing?
- How do I interpret driver rankings for my propensity or regression model?
- How do I interpret error metrics for my propensity model?
- What is a confusion matrix?
- What is an ROC curve?
- How do I interpret the propensity bin comparison chart?
- How is prediction performance monitored for my propensity model?
- Which algorithm should I pick for my regression model?
- How do I interpret error metrics for my regression model?
- How do I interpret coefficients for my regression model?
- How do I interpret lag estimates for my regression model?
- How do I interpret saturation estimates for my regression model?
- How is prediction performance monitored for my regression model?