Model | Accuracy |
---|---|
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 | \(57.8\% (0.027)\) |