Precisão é definida como: p = true positives / (true positives + false positives) É verdade que, como true positivese false positivesabordagem 0, a precisão se aproxima de 1? Mesma pergunta para recall: r = true positives / (true positives + false negatives) No momento, estou implementando...
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Quais são os valores corretos para precisão e rechamada em casos extremos?
precision-recall
data-visualization
logarithm
references
r
networks
data-visualization
standard-deviation
probability
binomial
negative-binomial
r
categorical-data
aggregation
plyr
survival
python
regression
r
t-test
bayesian
logistic
data-transformation
confidence-interval
t-test
interpretation
distributions
data-visualization
pca
genetics
r
finance
maximum
probability
standard-deviation
probability
r
information-theory
references
computational-statistics
computing
references
engineering-statistics
t-test
hypothesis-testing
independence
definition
r
censoring
negative-binomial
poisson-distribution
variance
mixed-model
correlation
intraclass-correlation
aggregation
interpretation
effect-size
hypothesis-testing
goodness-of-fit
normality-assumption
small-sample
distributions
regression
normality-assumption
t-test
anova
confidence-interval
z-statistic
finance
hypothesis-testing
mean
model-selection
information-geometry
bayesian
frequentist
terminology
type-i-and-ii-errors
cross-validation
smoothing
splines
data-transformation
normality-assumption
variance-stabilizing
r
spss
stata
python
correlation
logistic
logit
link-function
regression
predictor
pca
factor-analysis
r
bayesian
maximum-likelihood
mcmc
conditional-probability
statistical-significance
chi-squared
proportion
estimation
error
shrinkage
application
steins-phenomenon