# compute.acc

From mobForest v1.3.1
by Kasey Jones

##### Predictive accuracy estimates across trees for logistic regression model

Compute predictive accuracy of response variable with binary outcome. The function takes observed and predicted binary responses as input and computes proportion of observations classified in same group.

##### Usage

`compute.acc(response, predictions, prob_cutoff = 0.5)`

##### Arguments

- response
A vector of binary outcome.

- predictions
A matrix of predicted probabilities (logit model) for out-of-bag observations for each tree.

- prob_cutoff
The threshold for predicting 1's & 0's.

##### Value

Predictive accuracy estimate ranging between 0 and 1.

##### Examples

```
# NOT RUN {
response <- as.data.frame( c(rep(0, 10000), rep(1, 10000)))
predictions <-
matrix(nrow = 20000, ncol = 3,
data = c(rep(.1, 15000), rep(.8, 5000), rep(.1, 15000),
rep(.8, 5000), rep(.1, 15000), rep(.8, 5000)))
compute.acc(response, predictions, prob_cutoff = .5)
# }
```

*Documentation reproduced from package mobForest, version 1.3.1, License: GPL (>= 2)*

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