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Table 1 Summary of classification algorithms evaluated on the training set

From: Diverse approaches to predicting drug-induced liver injury using gene-expression profiles

Classification algorithmscikit-learn implementationParameters selected after optimization
Multilayer Perceptronsklearn.neural_network.MLPClassifieractivation = ‘relu’
alpha = 0.0001
batch_size = ‘auto’
beta_1 = 0.9
beta_2 = 0.999
early_stopping = False
epsilon = 1e-08
hidden_layer_sizes = (30,30,30,30,30,30,30,30,30,30)
learning_rate = ‘constant’
learning_rate_init = 0.0376
max_iter = 200
momentum = 0.9
nesterovs_momentum = True
power_t = 0.5
random_state = None
shuffle = True
solver = ‘adam’
tol = 0.0001
validation_fraction = 0.1
warm_start = False
Gradient Boostingsklearn.ensemble. GradientBoostingClassifiercriterion = ‘friedman_mse’
init = None
learning_rate = 0.31
loss = ‘deviance
max_depth = 3
max_features = None
max_leaf_nodes = None
min_impurity_decrease = 0.0
min_impurity_split = None
min_samples_leaf = 1
min_samples_split = 2
min_weight_fraction_leaf = 0.0
n_estimators = 100
presort = ‘auto’
subsample = 1.0
warm_start = False
K-nearest Neighborsklearn.neighbors.KNeighborsClassifieralgorithm = ‘auto’
leaf_size = 30
metric = ‘minkowski’
metric_params = None
n_neighbors = 8
p = 2
weights = ‘distance’
Logistic Regressionsklearn.linear_model.LogisticRegressionC = 1.0
class_weight = None
dual = False
fit_intercept = True
intercept_scaling = 1
max_iter = 100
multi_class = ‘ovr’
penalty = ‘l2’
solver = ‘lbfgs’
tol = 0.0001
warm_start = False
Gaussian Naïve Bayessklearn.naive_bayes.GaussianNBpriors = None
Random Forestsklearn.ensemble. RandomForestClassifierbootstrap = False
class_weight = None
criterion = ‘gini’
max_depth = 9
min_samples_split = 2
min_samples_leaf = 1
min_weight_fraction_leaf = 0.0
max_features = ‘auto’
max_leaf_nodes = 25
min_impurity_decrease = 0.0
min_impurity_split = None
n_estimators = 25
oob_score = False
warm_start = False
Support Vector Machinessklearn.svm. SVCC = 1.0
class_weight = None
coef0 = 0.0
decision_function_shape = ‘ovr’
degree = 3
gamma = ‘auto’
kernel = ‘rbf’
max_iter = − 1
probability = False
shrinking = True
tol = 0.001
Voting-based Ensemblesklearn.ensemble. VotingClassifierflatten_transform = True
voting = ‘soft’
weights = ‘None’
  1. In Phase I, we employed 7 classification algorithms and a voting-based method that integrated predictions from the individual classifiers. The first two columns indicate a name for each algorithm and the scikit-learn implementation that we used for each algorithm. Using an ad hoc approach, we evaluated many hyperparameters via cross validation on the training set and selected a hyperparameter combination for each algorithm that performed best. Non-default parameters are bolded. Hyperparameters that do not fundamentally affect algorithm behavior—such as the number of parallel jobs—are not shown