Tree Methods

# Tree Methods API Reference

## Functions

``(M::CART)(x)``

This is a universal CART object predict function.

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``ClassificationTree(x, y; gainfn = entropy, maxdepth = 4, minbranchsize = 3)``

Builds a CART object using either gini or entropy as a partioning method. Y must be a one hot encoded 2-Array. Predictions can be formed by calling the following function from the CART object: (M::CART)(x).

*Note: this is a purely nonrecursive decision tree. The julia compiler doesn't like storing structs of nested things. I wrote it the recursive way in the past and it was quite slow, I think this is true also of interpretted languages like R/Python...So here it is, nonrecursive tree's!

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``OneHotOdds(Y)``

Calculates the odds of a one-hot formatted probability matrix. Returns a tuple.

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``RegressionTree(x, y; gainfn = ssd, maxdepth = 4, minbranchsize = 3)``

Builds a CART object using ssd as a partioning method. Y must be a one column Array. Predictions can be formed by calling the following function from the CART object: (M::CART)(x).

*Note: this is a purely nonrecursive decision tree. The julia compiler doesn't like storing structs of nested things. I wrote it the recursive way in the past and it was quite slow, I think this is true also of interpretted languages like R/Python...So here it is, nonrecursive tree's!

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``entropy(v)``

Calculates the Shannon-Entropy of a probability vector `v`. Returns a scalar. A common gain function used in tree methods.

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``gini(p)``

Calculates the GINI coefficient of a probability vector `p`. Returns a scalar. A common gain function used in tree methods.

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``ssd(p)``

Calculates the sum squared deviations from a decision tree split. Accepts a vector of values, and the mean of that vector. Returns a scalar. A common gain function used in tree methods.

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