scorecard: perf_eva – R documentation

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perf_eva

Binomial Metrics

Description

perf_eva calculates metrics to evaluate the performance of binomial classification model. It can also creates confusion matrix and model performance graphics.

Usage

perf_eva(pred, label, title = NULL, binomial_metric = c("mse", "rmse",
  "logloss", "r2", "ks", "auc", "gini"), confusion_matrix = FALSE,
  threshold = NULL, show_plot = c("ks", "lift"), pred_desc = TRUE,
  positive = "bad|1", ...)

Arguments

`pred`	A list or vector of predicted probability or score.
`label`	A list or vector of label values.
`title`	The title of plot. Defaults to NULL.
`binomial_metric`	Defaults to c('mse', 'rmse', 'logloss', 'r2', 'ks', 'auc', 'gini'). If it is NULL, then no metric will calculated.
`confusion_matrix`	Logical, whether to create a confusion matrix. Defaults to TRUE.
`threshold`	Confusion matrix threshold. Defaults to the pred on maximum F1.
`show_plot`	Defaults to c('ks', 'roc'). Accepted values including c('ks', 'lift', 'gain', 'roc', 'lz', 'pr', 'f1', 'density').
`pred_desc`	whether to sort the argument of pred in descending order. Defaults to TRUE.
`positive`	Value of positive class. Defaults to "bad\|1".
`...`	Additional parameters.

Details

Accuracy = true positive and true negative/total cases

Error rate = false positive and false negative/total cases

TPR, True Positive Rate(Recall or Sensitivity) = true positive/total actual positive

PPV, Positive Predicted Value(Precision) = true positive/total predicted positive

TNR, True Negative Rate(Specificity) = true negative/total actual negative = 1-FPR

NPV, Negative Predicted Value = true negative/total predicted negative

Value

A list of binomial metric, confusion matrix and graphics

Examples

# data preparing ------
# load germancredit data
data("germancredit")
# filter variable via missing rate, iv, identical value rate
dt_f = var_filter(germancredit, "creditability")
# breaking dt into train and test
dt_list = split_df(dt_f, "creditability")
label_list = lapply(dt_list, function(x) x$creditability)

# woe binning ------
bins = woebin(dt_list$train, "creditability")
# converting train and test into woe values
dt_woe_list = lapply(dt_list, function(x) woebin_ply(x, bins))

# glm ------
m1 = glm(creditability ~ ., family = binomial(), data = dt_woe_list$train)
# vif(m1, merge_coef = TRUE)
# Select a formula-based model by AIC
m_step = step(m1, direction="both", trace=FALSE)
m2 = eval(m_step$call)
# vif(m2, merge_coef = TRUE)

# predicted proability
pred_list = lapply(dt_woe_list, function(x) predict(m2, type = 'response', x))

# scorecard ------
card = scorecard(bins, m2)

# credit score, only_total_score = TRUE
score_list = lapply(dt_list, function(x) scorecard_ply(x, card))
# credit score, only_total_score = FALSE
score_list2 = lapply(dt_list, function(x) scorecard_ply(x, card,
  only_total_score=FALSE))


###### perf_eva examples ######
# Example I, one datset
## predicted p1
perf_eva(pred = pred_list$train, label=dt_list$train$creditability,
  title = 'train')
## predicted score
# perf_eva(pred = score_list$train, label=dt_list$train$creditability,
#   title = 'train')

# Example II, multiple datsets
## predicted p1
perf_eva(pred = pred_list, label = label_list,
 show_plot = c('ks', 'lift', 'gain', 'roc', 'lz', 'pr', 'f1', 'density'))
## predicted score
# perf_eva(score_list, label_list)


###### perf_psi examples ######
# Example I # only total psi
psi1 = perf_psi(score = score_list, label = label_list)
psi1$psi  # psi data frame
psi1$pic  # pic of score distribution

# Example II # both total and variable psi
psi2 = perf_psi(score = score_list, label = label_list)
# psi2$psi  # psi data frame
# psi2$pic  # pic of score distribution


###### gains_table examples ######
# Example I, input score and label can be a list or a vector
g1 = gains_table(score = score_list$train, label = label_list$train)
g2 = gains_table(score = score_list, label = label_list)

# Example II, specify the bins number and type
g3 = gains_table(score = score_list, label = label_list, bin_num = 20)
g4 = gains_table(score = score_list, label = label_list, method = 'width')

scorecard

Credit Risk Scorecard

v0.3.2

MIT + file LICENSE

Authors

Shichen Xie [aut, cre]

Initial release