def main():
data, targets = rf.read_letters()
clf = RandomForestClassifier(n_estimators=10,
max_depth=None,
min_samples_split=1,
random_state=0)
scores = cross_val_score(clf, data, targets)
print("Forest_Letters: ", end="")
print(scores.mean() * 100)
data, targets = rf.read_abalone()
clf = RandomForestClassifier(n_estimators=10,
max_depth=None,
min_samples_split=1,
random_state=0)
scores = cross_val_score(clf, data, targets)
print("Forest_Abalone: ", end="")
print(scores.mean() * 100)
data, targets = rf.read_lungs()
clf = RandomForestClassifier(n_estimators=10,
max_depth=None,
min_samples_split=1,
random_state=0)
scores = cross_val_score(clf, data, targets)
print("Forest_Lungs: ", end="")
print(scores.mean() * 100)
def main():
data, targets = rf.read_letters()
clf = BaggingClassifier(KNeighborsClassifier(),
max_samples=0.5,
max_features=0.5)
scores = cross_val_score(clf, data, targets)
print("Bagging_Letters: ", end="")
print(scores.mean() * 100)
data, targets = rf.read_abalone()
clf = BaggingClassifier(KNeighborsClassifier(),
max_samples=0.5,
max_features=0.5)
scores = cross_val_score(clf, data, targets)
print("Bagging_Abalone: ", end="")
print(scores.mean() * 100)
data, targets = rf.read_lungs()
clf = BaggingClassifier(KNeighborsClassifier(),
max_samples=0.5,
max_features=0.5)
scores = cross_val_score(clf, data, targets)
print("Bagging_Lungs: ", end="")
print(scores.mean() * 100)
def main():
data, targets = rf.read_letters()
clf = KNeighborsClassifier(n_neighbors=2, algorithm='ball_tree')
scores = cross_val_score(clf, data, targets)
print("KNN_Letters: ", end="")
print(scores.mean() * 100)
data, targets = rf.read_abalone()
clf = KNeighborsClassifier(n_neighbors=2, algorithm='ball_tree')
scores = cross_val_score(clf, data, targets)
print("KNN_Abalone: ", end="")
print(scores.mean() * 100)
data, targets = rf.read_lungs()
clf = KNeighborsClassifier(n_neighbors=2, algorithm='ball_tree')
scores = cross_val_score(clf, data, targets)
print("KNN_Lungs: ", end="")
print(scores.mean() * 100)
def main():
data, targets = rf.read_letters()
clf = AdaBoostClassifier(n_estimators=100, learning_rate=.007)
scores = cross_val_score(clf, data, targets)
print("Adaboost_Letters: ", end="")
print(scores.mean() * 100)
data, targets = rf.read_abalone()
clf = AdaBoostClassifier(n_estimators=100, learning_rate=.12)
scores = cross_val_score(clf, data, targets)
print("Adaboost_Abalone: ", end="")
print(scores.mean() * 100)
data, targets = rf.read_lungs()
clf = AdaBoostClassifier(n_estimators=100, learning_rate=.1)
scores = cross_val_score(clf, data, targets)
print("Adadboost_Lungs: ", end="")
print(scores.mean() * 100)
def main():
data, targets = rf.read_letters()
clf = tree.DecisionTreeClassifier(criterion='entropy')
scores = cross_val_score(clf, data, targets)
print("DecisionTree_Letters", end="")
print(scores.mean() * 100)
data, targets = rf.read_abalone()
clf = tree.DecisionTreeClassifier(criterion='entropy')
scores = cross_val_score(clf, data, targets)
print("DecisionTree_Abalone", end="")
print(scores.mean() * 100)
data, targets = rf.read_lungs()
clf = tree.DecisionTreeClassifier(criterion='entropy')
scores = cross_val_score(clf, data, targets)
print("DecisionTree_Lungs", end="")
print(scores.mean() * 100)
def main():
data, targets = rf.read_letters()
clf = svm.SVC()
scores = cross_val_score(clf, data, targets)
print("SVM_Letters: ", end="")
print(scores.mean() * 100)
data, targets = rf.read_abalone()
clf = svm.SVC()
scores = cross_val_score(clf, data, targets)
print("SVM_Abalone: ", end="")
print(scores.mean() * 100)
data, targets = rf.read_lungs()
clf = svm.SVC()
scores = cross_val_score(clf, data, targets)
print("SVM_Lungs: ", end="")
print(scores.mean() * 100)