TABLE IX.

Optimal Hyperparameters for LINCS Prostate Only MOA

GCNN	regularization	4.00e–3
	num_epochs	200
	Fs	[[25]]
	batch_size	55
	M	[168, 14, 9]
	Ks	[[15]]
	ps	[[2]]
	pool	mpool1
	learning_rate	5.00e–3
	decay_steps	415
	decay_rate	9.50e–1
	momentum	9.70e–1
	dropout	5.00e–1
FF-ANN	learning_rate	invscaling
	nesterovs_momentum	True
	hidden_layer_sizes	[997]
	learning_rate_init	5.53e–2
	momentum	8.67e–1
	early_stopping	False
	alpha	8.20e–1
	power_t	2.26e–1
	activation	relu
KNNs	metric	canberra
	weights	distance
	n_neighbors	13
Linear Classifier	eta0	3.17e–4
	l1_ratio	4.06e–1
	tol	1.00e–5
	penalty	l1
	learning_rate	invscaling
	alpha	1.23e–3
	n_jobs	−1
	power_t	1.84e–1
	loss	log
Random Forest	min_samples_split	2
	criterion	entropy
	min_weight_fraction_leaf	6.01e–5
	min_samples_leaf	2
	max_depth	None
	min_impurity_decrease	3.68e–4
	max_leaf_nodes	None
	n_estimators	53
Decision Tree	min_impurity_decrease	1.46e–3
	min_samples_leaf	1
	min_weight_fraction_leaf	1.81e–4
	max_depth	25
	max_features	250
	criterion	entropy
	max_leaf_nodes	None
	min_samples_split	2
	splitter	best