Model Eğitimleri

main-model-train.py

#Model Eğitimleri.

#MultinomialNB Best random_state=3305
X_train, X_test, y_train, y_test = train_test_split(ogrenme_seti.iloc[:, 0:4], ogrenme_seti.iloc[:,-1:], test_size=0.25,random_state=3305)
clf.fit(X_train,y_train.values.ravel())
y_pred_test = clf.predict(X_test)
print("Naive Bayes::\n", confusion_matrix(y_test,y_pred_test), "\n")
f1_1 = f1_score(y_test,y_pred_test,average='macro')
print(classification_report(y_test,y_pred_test))
print("Accuracy: ",accuracy_score(y_test,y_pred_test))
print("F1 score: ",f1_1)

#Lojistik Regresyon Best random_state=3921
X_train, X_test, y_train, y_test = train_test_split(ogrenme_seti.iloc[:, 0:4], ogrenme_seti.iloc[:,-1:], test_size=0.25,random_state=3921)
lr.fit(X_train,y_train.values.ravel())
y_pred_test = lr.predict(X_test)
print("\nLogisticRegression::\n", confusion_matrix(y_test,y_pred_test), "\n")
f1_2 = f1_score(y_test,y_pred_test,average='macro')
print(classification_report(y_test,y_pred_test))
print("Accuracy: ",accuracy_score(y_test,y_pred_test))
print("F1 score: ",f1_2)

#Karar Ağacı Best random_state=8679
X_train, X_test, y_train, y_test = train_test_split(ogrenme_seti.iloc[:, 0:4], ogrenme_seti.iloc[:,-1:], test_size=0.25,random_state=8679)
dtc.fit(X_train,y_train)
y_pred_test = dtc.predict(X_test)
print("\nDecisionTreeClassifier::\n", confusion_matrix(y_test,y_pred_test), "\n")
f1_3 = f1_score(y_test,y_pred_test,average='macro')
print(classification_report(y_test,y_pred_test))
print("Accuracy: ",accuracy_score(y_test,y_pred_test))
print("F1 score: ",f1_3)

#Rassal Orman Best random_state=6040
X_train, X_test, y_train, y_test = train_test_split(ogrenme_seti.iloc[:, 0:4], ogrenme_seti.iloc[:,-1:], test_size=0.25, random_state=6040)
rfc.fit(X_train,y_train.values.ravel())
y_pred_test = rfc.predict(X_test)
print("\nRandomForestClassifier::\n", confusion_matrix(y_test,y_pred_test), "\n")
f1_4 = f1_score(y_test,y_pred_test,average='macro')
print(classification_report(y_test,y_pred_test))
print("Accuracy: ",accuracy_score(y_test,y_pred_test))
print("F1 score: ",f1_4)

#Gradyan Arttırma Best random_state=5211
X_train, X_test, y_train, y_test = train_test_split(ogrenme_seti.iloc[:, 0:4], ogrenme_seti.iloc[:,-1:], test_size=0.25, random_state=5211)
gradient.fit(X_train,y_train.values.ravel())
y_pred_test = gradient.predict(X_test)
print("\nGradientBoostingClassifier::\n", confusion_matrix(y_test,y_pred_test), "\n")
f1_5 = f1_score(y_test,y_pred_test,average='macro')
print(classification_report(y_test,y_pred_test))
print("Accuracy: ",accuracy_score(y_test,y_pred_test))
print("F1 score: ",f1_5)

#XGB Best random_state=6366
X_train, X_test, y_train, y_test = train_test_split(ogrenme_seti.iloc[:, 0:4], ogrenme_seti.iloc[:,-1:], test_size=0.25, random_state=6366)
xgb.fit(X_train,y_train.values.ravel())
y_pred_test = xgb.predict(X_test)
print("\nXGBClassifier::\n", confusion_matrix(y_test,y_pred_test), "\n")
f1_6 = f1_score(y_test,y_pred_test,average='macro')
print(classification_report(y_test,y_pred_test))
print("Accuracy: ",accuracy_score(y_test,y_pred_test))
print("F1 score: ",f1_6)