SOA Exam PA Exam Review

SOA Exam PA Exam Review

1. Explain the difference between descriptive, predictive, and prescriptive modeling: Descriptive - focuses on what happened in the past and aims to describe the observed trends by identifying relationships between variables. Predictive - What will happen in the future and making accurate predictions. Prescriptive - Combo of optimization and simulation to investigate the impact of prescribed actions in different scenarios. 2. Explain the difference between supervised and unsupervised learning: Su- pervised learning has a target variable and the goal is to understand the relationship between it and the predictors or make predictions. Unsupervised learning has no target variable, and we are interested in relationships and structures between variables. 3. Explain how stratified sampling contributes to a more representative sam- ple than random sampling: Divides the population into non-overlapping groups in a non-random fashion. Allows for all stratum to be represented in collected data. 4. Explain three data quality issues one should examine in practice: Reason- ableness - data values need to be reasonable in their recording. Consistency - data must be inputted consistently with the same levels, units, cate- gorical variables recorded in consistent manner. Sufficient documentation - Have overall description, variable description, updates, accountability, and how to manage the dataset. 5. Explain the problem with target leakage in predictive analytics: Predictors that leak information about the target variable but will not be available when the model is applied in practice. 6. Explain how to use a time variable to make the training/test set split and the advantage of doing so: Allocate the older observations to the training set and the more recent to the test set. This is useful for evaluating how well a model extrapolates time trends observed in the part to future, unseen years. 7. Explain what hyperparameters are and why they are important for a pre- dictive model: Hyperparameters are values that control some aspect of the fitting process. Typically involves optimizing an objective function or in the set of con- straints. Supply their values in advance. 8. Explain the difference between bias and variance in a predictive analytic context: Bias is the difference between the expected value of our fitted function and the true value of the signal function. Variance quantifies the amount by which our fitted function would change if we estimated it using a different training set. 9. Explain the difference between variables and features in a predictive analyt- ic context: Variables are a predictor in a model