`acc`

defines overall accuracy
as the probability of correspondence between a positive decision
and true condition (i.e., the proportion of correct classification
decisions or of `dec_cor`

cases).

`acc`

An object of class `numeric`

of length 1.

Importantly, correct decisions `dec_cor`

are not necessarily positive decisions `dec_pos`

.

Understanding or obtaining the accuracy metric `acc`

:

Definition:

`acc`

is the (non-conditional) probability:`acc = p(dec_cor) = dec_cor/N`

or the base rate (or baseline probability) of a decision being correct, but not necessarily positive.

`acc`

values range from 0 (no correct decision/prediction) to 1 (perfect decision/prediction).Computation:

`acc`

can be computed in several ways:(a) from

`prob`

:`acc = (prev x sens) + [(1 - prev) x spec]`

(b) from

`freq`

:`acc = dec_cor/N = (hi + cr)/(hi + mi + fa + cr)`

(c) as complement of the error rate

`err`

:`acc = 1 - err`

When frequencies in

`freq`

are not rounded, (b) coincides with (a) and (c).Perspective:

`acc`

classifies a population of`N`

individuals by accuracy/correspondence (`acc = dec_cor/N`

).`acc`

is the "by accuracy" or "by correspondence" counterpart to`prev`

(which adopts a "by condition" perspective) and to`ppod`

(which adopts a "by decision" perspective).Alternative names: base rate of correct decisions, non-erroneous cases

In terms of frequencies,

`acc`

is the ratio of`dec_cor`

(i.e.,`hi + cr`

) divided by`N`

(i.e.,`hi + mi`

+`fa + cr`

):`acc = dec_cor/N = (hi + cr)/(hi + mi + fa + cr)`

Dependencies:

`acc`

is a feature of both the environment (true condition) and of the decision process or diagnostic procedure. It reflects the correspondence of decisions to conditions.

See `accu`

for other accuracy metrics
and several possible interpretations of accuracy.

Consult Wikipedia:Accuracy_and_precision for additional information.

`comp_acc`

computes accuracy from probabilities;
`accu`

lists all accuracy metrics;
`comp_accu_prob`

computes exact accuracy metrics from probabilities;
`comp_accu_freq`

computes accuracy metrics from frequencies;
`comp_sens`

and `comp_PPV`

compute related probabilities;
`is_extreme_prob_set`

verifies extreme cases;
`comp_complement`

computes a probability's complement;
`is_complement`

verifies probability complements;
`comp_prob`

computes current probability information;
`prob`

contains current probability information;
`is_prob`

verifies probabilities.

Other probabilities: `FDR`

, `FOR`

,
`NPV`

, `PPV`

, `err`

,
`fart`

, `mirt`

,
`ppod`

, `prev`

,
`sens`

, `spec`

Other metrics: `accu`

,
`comp_accu_freq`

,
`comp_accu_prob`

, `comp_acc`

,
`comp_err`

, `err`

acc <- .50 # sets a rate of correct decisions of 50% acc <- 50/100 # (dec_cor) for 50 out of 100 individuals is_prob(acc) # TRUE#> [1] TRUE