mlbench.cuboids
(Package: mlbench) :
Cuboids: A 3 Dimensional Problem
The inputs of the cuboids problem are uniformly distributed on a 3-dimensional space within 3 cuboids and a small cube in the middle of them.
● Data Source:
CranContrib
● Keywords: datagen
● Alias: mlbench.cuboids
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mlbench.xor
(Package: mlbench) :
Continuous XOR Benchmark Problem
The inputs of the XOR problem are uniformly distributed on the d-dimensional cube with corners {+-1}. Each pair of opposite corners form one class, hence the total number of classes is 2^(d-1)
● Data Source:
CranContrib
● Keywords: datagen
● Alias: mlbench.xor
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Converts x (which is basically a list) to a dataframe.
● Data Source:
CranContrib
● Keywords: manip
● Alias: as.data.frame.mlbench
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The inputs of the circle problem are uniformly distributed on the d-dimensional cube with corners {+-1}. This is a 2-class problem: The first class is a d-dimensional ball in the middle of the cube, the remainder forms the second class. The size of the ball is chosen such that both classes have equal prior probability 0.5.
● Data Source:
CranContrib
● Keywords: datagen
● Alias: mlbench.circle
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The inputs of the spirals problem are points on two entangled spirals. If sd>0, then Gaussian noise is added to each data point. mlbench.1spiral creates a single spiral.
● Data Source:
CranContrib
● Keywords: datagen
● Alias: mlbench.1spiral, mlbench.spirals
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A Gaussian, square, triangle and wave in 2 dimensions.
● Data Source:
CranContrib
● Keywords: datagen
● Alias: mlbench.shapes
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The regression problem Friedman 3 as described in Friedman (1991) and Breiman (1996). Inputs are 4 independent variables uniformly distrtibuted over the ranges
● Data Source:
CranContrib
● Keywords: datagen
● Alias: mlbench.friedman3
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Each of the cl classes consists of a 2-dimensional Gaussian. The centers are equally spaced on a circle around the origin with radius r.
● Data Source:
CranContrib
● Keywords: datagen
● Alias: mlbench.2dnormals
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The regression problem Friedman 2 as described in Friedman (1991) and Breiman (1996). Inputs are 4 independent variables uniformly distrtibuted over the ranges
● Data Source:
CranContrib
● Keywords: datagen
● Alias: mlbench.friedman2
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The inputs of the ringnorm problem are points from two Gaussian distributions. Class 1 is multivariate normal with mean 0 and covariance 4 times the identity matrix. Class 2 has unit covariance and mean (a,a,…,a), a=d^{-0.5}.
● Data Source:
CranContrib
● Keywords: datagen
● Alias: mlbench.ringnorm
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providing 
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