|
logistic regression
|
\(67.7\% (0.034)\)
|
|
penalized logistic regression
|
\(70.8\% (0.022)\)
|
|
cumulative probability model
|
\(68.6\% (0.035)\)
|
|
random forest
|
\(59.2\% (0.034)\)
|
|
weighted subspace random forest
|
\(59.3\% (0.033)\)
|
|
SVM with class weight
|
\(70.2\% (0.043)\)
|
|
SVM with polynomial kernel
|
\(66.3\% (0.041)\)
|
|
SVM with radial kernel
|
\(63.7\% (0.041)\)
|
|
K-nearest neighbor
|
\(58.2\% (0.037)\)
|
|
LDA
|
\(69.6\% (0.048)\)
|
|
sparsed LDA
|
\(58.8\% (0.036)\)
|
|
naive Bayes
|
\(64.4\% (0.024)\)
|
|
Bayes generalized linear model
|
\(68.0\% (0.031)\)
|
|
Gaussian process with polynomial kernel
|
\(70.1\% (0.035)\)
|
|
Gaussian process with radial kernel
|
\(65.2\% (0.029)\)
|
|
Neural network
|
\(62.8\% (0.043)\)
|
|
Monotone multi-layer perceptron neural network
|
\(69.2\% (0.026)\)
|
|
model average neural network
|
\(65.1\% (0.035)\)
|
|
stochastic gradient boosting
|
\(57.8\% (0.027)\)
|
