Exam SRM – Statistics for Risk Modeling
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SOA (Society of Actuaries) の学習者向けの無料Exam SRM – Statistics for Risk Modeling学習ノート。各章のリンクから、要点・例題・練習問題への導線にアクセスできます。
Basics of Statistical Learning
Supervised vs. unsupervised and regression vs. classification
Assessing model accuracy
The bias-variance tradeoff
Training set vs. test set validation
k-fold cross-validation
Leave-one-out cross-validation
Linear Models
Ordinary least squares vs. generalized linear model assumptions
The exponential family and link functions
Interpreting parameters in a business context
Diagnostic tests of model fit and assumption checking
Variable transformations and interactions
Model selection criteria: t and F tests, AIC, BIC, and likelihood ratio test
Predicted values, confidence intervals, and prediction intervals
Regularized regression and K-nearest neighbors
Time Series Models
Stochastic time series: random walks, stationarity, and autocorrelation
Exponential smoothing models
Autoregressive models
Autoregressive conditionally heteroskedastic models
Predicted values and confidence intervals
Decision Trees
Constructing and pruning decision trees
Classification trees
Regression trees
Bagging, boosting, and random forests
Comparing decision trees to linear models
Unsupervised Learning Techniques
Principal components and how they are calculated
Interpreting principal components analysis
K-means clustering
Hierarchical clustering
Deciding the number of clusters
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