Loading project/__pycache__/parameter_opt.cpython-37.pyc +1.64 KiB (3.03 KiB) File changed.No diff preview for this file type. View original file View changed file project/classifier.py +2 −2 Original line number Original line Diff line number Diff line Loading @@ -86,9 +86,9 @@ rnd_forr = RandomForestClassifier(criterion='entropy', max_depth= 9, min_samples naive_bayes = CategoricalNB() # no smoothing naive_bayes = CategoricalNB() # no smoothing naive_bayes_ws = CategoricalNB(alpha=1.0) # smoothing with alpha = 1.0 naive_bayes_ws = CategoricalNB(alpha=1.0) # smoothing with alpha = 1.0 # SVM Kernel # SVM Kernel svc = SVC(kernel="rbf", gamma=5, decision_function_shape='ovo', C=1) svc = SVC(kernel="rbf", gamma=1, decision_function_shape='ovo', C=1) # Neuronal Network # Neuronal Network mlp = MLPClassifier(alpha=1, max_iter=1000) mlp = MLPClassifier(activation="tanh" ,alpha=0.5380556432206247,hidden_layer_sizes=(473, 441), learning_rate="constant", solver="sgd", max_iter=1000) # choose classifier # choose classifier classifiers = [ classifiers = [ Loading Loading
project/__pycache__/parameter_opt.cpython-37.pyc +1.64 KiB (3.03 KiB) File changed.No diff preview for this file type. View original file View changed file
project/classifier.py +2 −2 Original line number Original line Diff line number Diff line Loading @@ -86,9 +86,9 @@ rnd_forr = RandomForestClassifier(criterion='entropy', max_depth= 9, min_samples naive_bayes = CategoricalNB() # no smoothing naive_bayes = CategoricalNB() # no smoothing naive_bayes_ws = CategoricalNB(alpha=1.0) # smoothing with alpha = 1.0 naive_bayes_ws = CategoricalNB(alpha=1.0) # smoothing with alpha = 1.0 # SVM Kernel # SVM Kernel svc = SVC(kernel="rbf", gamma=5, decision_function_shape='ovo', C=1) svc = SVC(kernel="rbf", gamma=1, decision_function_shape='ovo', C=1) # Neuronal Network # Neuronal Network mlp = MLPClassifier(alpha=1, max_iter=1000) mlp = MLPClassifier(activation="tanh" ,alpha=0.5380556432206247,hidden_layer_sizes=(473, 441), learning_rate="constant", solver="sgd", max_iter=1000) # choose classifier # choose classifier classifiers = [ classifiers = [ Loading
