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 AVA?
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?