# Model Evaluation

    library(bayesrules)

For Bayesian model evaluation, the bayesrules package has three functions `prediction_summary()`, `classification_summary()` and `naive_classification_summary()` as well as their cross-validation counterparts `prediction_summary_cv()`, `classification_summary_cv()`, and `naive_classification_summary_cv()` respectively.

Functions | Response | Model
--- | --- | ---
`prediction_summary()` `prediction_summary_cv()` | Quantitative | rstanreg
`classification_summary()` `classification_summary_cv()` | Binary | rstanreg
`naive_classification_summary()` `naive_classification_summary_cv()` | Categorical | naiveBayes

## Prediction Summary

Given a set of observed data including a quantitative response variable y and an rstanreg model of y, `prediction_summary()` returns 4 measures of the posterior prediction quality.

1. Median absolute prediction error (mae) measures the typical difference between the observed y values and their posterior predictive medians (stable = TRUE) or means (stable = FALSE).

2. Scaled mae (mae_scaled) measures the typical number of absolute deviations (stable = TRUE) or standard deviations (stable = FALSE) that observed y values fall from their predictive medians (stable = TRUE) or means (stable = FALSE).

3. and 4. within_50 and within_90 report the proportion of observed y values that fall within their posterior prediction intervals, the probability levels of which are set by the user. Although 50% and 90% are the defaults for the posterior prediction intervals, these probability levels can be changed with `prob_inner` and `prob_outer` arguments. The example below shows the 60% and 80% posterior prediction intervals.

    # Data generation
    example_data <- data.frame(x = sample(1:100, 20))
    example_data$y <- example_data$x*3 + rnorm(20, 0, 5)

    # rstanreg model
    example_model <- rstanarm::stan_glm(y ~ x,  data = example_data, refresh = FALSE)

    # Prediction Summary
    prediction_summary(example_model, example_data,
                       prob_inner = 0.6, prob_outer = 0.80,
                       stable = TRUE)
           mae mae_scaled within_60 within_80
    1 2.405058  0.8680121       0.6       0.9

Similarly, `prediction_summary_cv()` returns the 4 cross-validated measures of a model’s posterior prediction quality for each fold as well as a pooled result. The `k` argument represents the number of folds to use for cross-validation.

    prediction_summary_cv(model = example_model, data = example_data,
                          k = 2, prob_inner = 0.6, prob_outer = 0.80)
    $folds
      fold      mae mae_scaled within_60 within_80
    1    1 2.848569  0.5061318       0.7       1.0
    2    2 2.732608  0.5569211       0.6       0.9

    $cv
           mae mae_scaled within_60 within_80
    1 2.790589  0.5315264      0.65      0.95

## Classification Summary

Given a set of observed data including a binary response variable y and an rstanreg model of y, the `classification_summary()` function returns summaries of the model’s posterior classification quality. These summaries include a confusion matrix as well as estimates of the model’s sensitivity, specificity, and overall accuracy. The `cutoff` argument represents the probability cutoff to classify a new case as positive.

    # Data generation
    x <- rnorm(20)
    z <- 3*x
    prob <- 1/(1+exp(-z))
    y <- rbinom(20, 1, prob)
    example_data <- data.frame(x = x, y = y)

    # rstanreg model
    example_model <- rstanarm::stan_glm(y ~ x, data = example_data,
                                        family = binomial, refresh = FALSE)

    # Prediction Summary
    classification_summary(model = example_model, data = example_data, cutoff = 0.5)
    $confusion_matrix
     y 0 1
     0 9 1
     1 2 8

    $accuracy_rates

    sensitivity      0.80
    specificity      0.90
    overall_accuracy 0.85

The `classification_summary_cv()` returns the same measures but for cross-validated estimates. The `k` argument represents the number of folds to use for cross-validation.

    classification_summary_cv(model = example_model, data = example_data, k = 2, cutoff = 0.5)
    $folds
      fold sensitivity specificity overall_accuracy
    1    1         0.6         1.0              0.8
    2    2         1.0         0.8              0.9

    $cv
      sensitivity specificity overall_accuracy
    1         0.8         0.9             0.85

## Naive Classification Summary

Given a set of observed data including a categorical response variable y and a naiveBayes model of y, the `naive_classification_summary()` function returns summaries of the model’s posterior classification quality. These summaries include a confusion matrix as well as an estimate of the model’s overall accuracy.

    # Data
    data(penguins_bayes, package = "bayesrules")

    # naiveBayes model
    example_model <- e1071::naiveBayes(species ~ bill_length_mm, data = penguins_bayes)

    # Naive Classification Summary
    naive_classification_summary(model = example_model, data = penguins_bayes, y = "species")
    $confusion_matrix
       species       Adelie Chinstrap       Gentoo
        Adelie 95.39% (145) 0.00% (0)  4.61%   (7)
     Chinstrap  5.88%   (4) 8.82% (6) 85.29%  (58)
        Gentoo  6.45%   (8) 4.84% (6) 88.71% (110)

    $overall_accuracy
    [1] 0.7587209

Similarly `naive_classification_summary_cv()` returns the cross validated confusion matrix. The `k` argument represents the number of folds to use for cross-validation.

    naive_classification_summary_cv(model = example_model, data = penguins_bayes,
                                    y = "species", k = 2)
    $folds
      fold    Adelie Chinstrap    Gentoo overall_accuracy
    1    1 0.9634146   0.09375 0.8965517        0.7790698
    2    2 0.9428571   0.00000 0.9242424        0.7383721

    $cv
       species       Adelie Chinstrap       Gentoo
        Adelie 95.39% (145) 0.00% (0)  4.61%   (7)
     Chinstrap  5.88%   (4) 4.41% (3) 89.71%  (61)
        Gentoo  6.45%   (8) 2.42% (3) 91.13% (113)