Description Usage Arguments Value Note References Examples

An implementation of the AdaBoost algorithm from Freund and Shapire (1997) applied to decision tree classifiers.

1 2 |

`X` |
A matrix of continuous predictors. |

`y` |
A vector of responses with entries in |

`tree_depth` |
The depth of the base tree classifier to use. |

`n_rounds` |
The number of rounds of boosting to use. |

`verbose` |
Whether to print the number of iterations. |

`control` |
A |

Returns an object of class `adaboost`

containing the following values:

`alphas` |
Weights computed in the adaboost fit. |

`trees` |
The trees constructed in each round of boosting. Storing trees allows one to make predictions on new data. |

`confusion_matrix` |
A confusion matrix for the in-sample fits. |

Trees are grown using the CART algorithm implemented in the `rpart`

package. In order to conserve memory, the only parts of the fitted
tree objects that are retained are those essential to making predictions.
In practice, the number of rounds of boosting to use is chosen by
cross-validation.

Freund, Y. and Schapire, R. (1997). A decision-theoretic generalization of online learning and an application to boosting, Journal of Computer and System Sciences 55: 119-139.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ```
## Not run:
# Generate data from the circle model
set.seed(111)
dat = circle_data(n = 500)
train_index = sample(1:500, 400)
ada = adaboost(dat$X[train_index,], dat$y[train_index], tree_depth = 2,
n_rounds = 200, verbose = TRUE)
print(ada)
yhat_ada = predict(ada, dat$X[-train_index,])
# calculate misclassification rate
mean(dat$y[-train_index] != yhat_ada)
## End(Not run)
``` |

```
JOUSBoost 2.1.0
Iteration: 10
Iteration: 20
Iteration: 30
Iteration: 40
Iteration: 50
Iteration: 60
Iteration: 70
Iteration: 80
Iteration: 90
Iteration: 100
Iteration: 110
Iteration: 120
Iteration: 130
Iteration: 140
Iteration: 150
Iteration: 160
Iteration: 170
Iteration: 180
Iteration: 190
Iteration: 200
AdaBoost: tree_depth = 2 rounds = 200
In-sample confusion matrix:
yhat
y -1 1
-1 237 15
1 26 122
[1] 0.18
```

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