Exam PA – Predictive Analytics

5个单元 · 22个章节

免费的Exam PA – Predictive Analytics学习笔记，专为SOA (Society of Actuaries)学生准备。下列每个章节都涵盖一个重点主题，附有例题与练习提示，可在 thinka 应用中延伸练习。

Predictive Analytics Problem Definition

Descriptive, predictive, and prescriptive analytics

Characteristics of predictive modeling problems

Bias, variance, model complexity, and the bias-variance trade-off

Translating a vague question into an analyzable one

Defining the problem: data, technology, business impact, and implementation

Identifying additional information and next steps

Data Exploration and Visualization

Structured and unstructured data types

Types of variables and predictive modeling terminology

Data design: time frame, sampling, and granularity

Key principles of constructing graphs

Univariate data exploration

Bivariate data exploration

Data Transformations and Unsupervised Learning Techniques

Creating features from existing data

Principal components analysis for data transformation

K-means and hierarchical clustering for data transformation

Generalized Linear Models

Selecting and validating a GLM for a business problem

Offsets and weights

Interpreting model coefficients and interaction terms

Hyperparameters for regularized regression

Tree-Based Models

Constructing, pruning, and validating regression and classification trees

Bagging and random forests

立即实践所学

不要只看笔记，用无限AI题库练习，即时获得批改反馈。加入超过100,000名正在提升成绩的学生。

立即开始练习查看定价

看完笔记了？用AI练习题测试自己

立即练习此课题