The MAD (Mean absolute deviation) gain, is a measure of set purity based on the deviation of dependent values present in the node. More...

#include <mad_gain.hpp>

Static Public Member Functions
template<bool UseWeights, typename VecType , typename WeightVecType >
static double	Evaluate (const VecType &values, const WeightVecType &weights, const size_t begin, const size_t end)
	Evaluate the mean absolute deviation gain from begin to end index. More...

template<bool UseWeights, typename VecType , typename WeightVecType >
static double	Evaluate (const VecType &values, const WeightVecType &weights)
	Evaluate the MAD gain on the complete vector. More...

Detailed Description

The MAD (Mean absolute deviation) gain, is a measure of set purity based on the deviation of dependent values present in the node.

This is same thing as negation of deviation of dependent variable from the mean in the node as we will try to maximize this quantity to maximize gain (and thus reduce absolute deviation of a set).

Member Function Documentation

◆ Evaluate() [1/2]

template<bool UseWeights, typename VecType , typename WeightVecType >

static double mlpack::tree::MADGain::Evaluate	(	const VecType &	values,
		const WeightVecType &	weights,
		const size_t	begin,
		const size_t	end
	)

inlinestatic

Evaluate the mean absolute deviation gain from begin to end index.

Note that gain can be slightly greater than 0 due to floating-point representation issues. Thus if you are checking for perfect fit, be sure to use 'gain >= 0.0'. Not 'gain == 0.0'. The values should always be of type arma::Row<double> or arma::rowvec.

Parameters

values	Set of values to evaluate MAD gain on.
weights	Weights associated to each value.
begin	Start index.
end	End index.

◆ Evaluate() [2/2]