def batch():
print("Tesing the accuracy of LogisticRegression(batch)...")
# Train model
clf = LogisticRegression()
clf.fit(X=X_train, y=y_train, lr=0.05, epochs=200)
# Model accuracy
get_acc(clf, X_test, y_test)
def batch():
print("Tesing the performance of LogisticRegression(batch)...")
# Train model
clf = LogisticRegression()
clf.fit(X=X_train, y=y_train, lr=0.05, epochs=200)
# Model evaluation
model_evaluation(clf, X_test, y_test)
def batch():
print("Tesing the performance of LogisticRegression(batch)...")
# Train model
clf = LogisticRegression()
clf.fit(data=data_train, label=label_train, learning_rate=0.1, epochs=1000)
# Model evaluation
model_evaluation(clf, data_test, label_test)
print(clf)
def stochastic():
print("Tesing the performance of LogisticRegression(stochastic)...")
# Train model
clf = LogisticRegression()
clf.fit(data=data_train, label=label_train, learning_rate=0.01, epochs=100,
method="stochastic", sample_rate=0.8)
# Model evaluation
model_evaluation(clf, data_test, label_test)
print(clf)
def stochastic():
print("Tesing the performance of LogisticRegression(stochastic)...")
# Train model
clf = LogisticRegression()
clf.fit(X=X_train, y=y_train, lr=0.01, epochs=100,
method="stochastic", sample_rate=0.8)
# Model evaluation
model_evaluation(clf, X_test, y_test)
print(clf)
def stochastic():
print("Tesing the accuracy of LogisticRegression(stochastic)...")
# Train model
clf = LogisticRegression()
clf.fit(X=X_train,
y=y_train,
lr=0.01,
epochs=200,
method="stochastic",
sample_rate=0.5)
# Model accuracy
get_acc(clf, X_test, y_test)