CART-based random forest implementation in Julia.

This package supports:

- Classification model
- Regression model
- Out-of-bag (OOB) error
- Feature importances
- Various configurable parameters

**Please be aware that this package is not yet fully examined implementation. You can use it at your own risk.**
And your bug report or suggestion is welcome!

Here you can try overview APIs available from the `RandomForests`

module.

`example.jl`

```
using RDatasets
using RandomForests
# classification
iris = dataset("datasets", "iris")
rf = RandomForestClassifier(n_estimators=100, max_features=:sqrt)
fit(rf, iris[1:4], iris[:Species])
@show predict(rf, iris[1:4])
@show oob_error(rf)
@show feature_importances(rf)
# regression
boston = dataset("MASS", "boston")
rf = RandomForestRegressor(n_estimators=5)
fit(rf, boston[1:13], boston[:MedV])
@show predict(rf, boston[1:13])
```

There are two separate models available in this package - classification and regression.
Each model has its own constructor which is trained by applying the `fit`

method.
You can configure these constructors with some keyword arguments listed below:

```
RandomForestClassifier(;n_estimators::Int=10,
max_features::Union(Integer, FloatingPoint, Symbol)=:sqrt,
max_depth=nothing,
min_samples_split::Int=2,
criterion::Symbol=:gini)
```

```
RandomForestRegressor(;n_estimators::Int=10,
max_features::Union(Integer, FloatingPoint, Symbol)=:third,
max_depth=nothing,
min_samples_split::Int=2)
```

`n_estimators`

: the number of weak estimators`max_features`

: the number of candidate features at each split- if
`Integer`

is given, the fixed number of features are used - if
`FloatingPoint`

is given, the proportion of given value (0.0, 1.0] are used - if
`Symbol`

is given, the number of candidate features is decided by a strategy`:sqrt`

:`ifloor(sqrt(n_features))`

`:third`

:`div(n_features, 3)`

- if
`max_depth`

: the maximum depth of each tree- the default argument
`nothing`

means there is no limitation of the maximum depth

- the default argument
`min_samples_split`

: the minimum number of sub-samples to try to split a node`criterion`

: the criterion of impurity measure (classification only)`:gini`

: Gini index`:entropy`

: Cross entropy

`RandomForestRegressor`

always uses the mean squared error for its impurity measure.
At the current moment, there is no configurable criteria for regression model.

Once you create a model, you can easily fit the model using the `fit`

method:

```
rf = RandomForestClassifier()
fit(rf, x, y)
```

Here the `fit`

methods takes three arguments:

`rf`

: the configured model of random forest (`RandomForestClassifier`

or`RandomForestRegressor`

)`x`

: the explanatory variables (`AbstractMatrix`

or`DataFrame`

)`y`

: the response variable (`AbstractVector`

)

Each column of `x`

is a feature of the input data and each row is an individual sample.
Each element of `y`

is an output corresponding a row of `x`

, so the number of row of `x`

and the
length of `y`

should match.
Note that even though the `DataFrame`

object is directly applicable to the `fit`

method, applying
a matrix is a much more efficient way to learn quickly.

The prediction using the fitted model is also easy. You can apply the new data to the `predict`

method:

```
predict(rf, new_x)
```

This returns a vector of predicted values.

The fitted model includes useful information calculated while learning.

`oob_error(rf)`

: the out-of-bag error, which is known as a good estimator of generalization error`feature_importances(rf)`

: relative importances of each explanatory variable

The feature importances are normalized values such that the sum of the importances is one.

- no parallelism

- DecisionTree.jl
- DecisionTree.jl is based on the ID3 (Iterative Dichotomiser 3) algorithm while RandomForests.jl uses CART (Classification And Regression Tree).

The algorithm and interface are highly inspired by those of scikit-learn.

05/27/2014

28 days ago

37 commits