# # Machine learning

## # Creating a Random Forest model

One example of machine learning algorithms is the Random Forest alogrithm (Breiman, L. (2001). Random Forests. **Machine Learning 45(5)**, p. 5-32). This algorithm is implemented in R according to Breiman's original Fortran implementation in the `randomForest`

package.

Random Forest classifier objects can be created in R by preparing the class variable as `factor`

, which is already apparent in the `iris`

data set. Therefore we can easily create a Random Forest by:

```
library(randomForest)
rf <- randomForest(x = iris[, 1:4],
y = iris$Species,
ntree = 500,
do.trace = 100)
rf
# Call:
# randomForest(x = iris[, 1:4], y = iris$Species, ntree = 500, do.trace = 100)
# Type of random forest: classification
# Number of trees: 500
# No. of variables tried at each split: 2
#
# OOB estimate of error rate: 4%
# Confusion matrix:
# setosa versicolor virginica class.error
# setosa 50 0 0 0.00
# versicolor 0 47 3 0.06
# virginica 0 3 47 0.06
```

parameters | Description |
---|---|

x | a data frame holding the describing variables of the classes |

y | the classes of the individual obserbations. If this vector is `factor` , a classification model is created, if not a regression model is created. |

ntree | The number of individual CART trees built |

do.trace | every ith step, the out-of-the-box errors overall and for each class are returned |