Aşağıda, Python ile şimdiye kadar yazdığımız sınıflandırma algoritmalarının şablonunu bulabilirsiniz:
#1. kutuphaneler
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.metrics import confusion_matrix
#2. Veri Onisleme
#2.1. Veri Yukleme
veriler = pd.read_csv('veriler.csv')
#pd.read_csv("veriler.csv")
x = veriler.iloc[:,1:4].values #bağımsız değişkenler
y = veriler.iloc[:,4:].values #bağımlı değişken
print(y)
#verilerin egitim ve test icin bolunmesi
from sklearn.cross_validation import train_test_split
x_train, x_test,y_train,y_test = train_test_split(x,y,test_size=0.33, random_state=0)
#verilerin olceklenmesi
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(x_train)
X_test = sc.transform(x_test)
# Buradan itibaren sınıflandırma algoritmaları başlar
# 1. Logistic Regression
from sklearn.linear_model import LogisticRegression
logr = LogisticRegression(random_state=0)
logr.fit(X_train,y_train) #egitim
y_pred = logr.predict(X_test) #tahmin
print(y_pred)
print(y_test)
#karmasiklik matrisi
cm = confusion_matrix(y_test,y_pred)
print(cm)
# 2. KNN
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=1, metric='minkowski')
knn.fit(X_train,y_train)
y_pred = knn.predict(X_test)
cm = confusion_matrix(y_test,y_pred)
print(cm)
# 3. SVC (SVM classifier)
from sklearn.svm import SVC
svc = SVC(kernel='poly')
svc.fit(X_train,y_train)
y_pred = svc.predict(X_test)
cm = confusion_matrix(y_test,y_pred)
print('SVC')
print(cm)
# 4. NAive Bayes
from sklearn.naive_bayes import GaussianNB
gnb = GaussianNB()
gnb.fit(X_train, y_train)
y_pred = gnb.predict(X_test)
cm = confusion_matrix(y_test,y_pred)
print('GNB')
print(cm)
# 5. Decision tree
from sklearn.tree import DecisionTreeClassifier
dtc = DecisionTreeClassifier(criterion = 'entropy')
dtc.fit(X_train,y_train)
y_pred = dtc.predict(X_test)
cm = confusion_matrix(y_test,y_pred)
print('DTC')
print(cm)
# 6. Random Forest
from sklearn.ensemble import RandomForestClassifier
rfc = RandomForestClassifier(n_estimators=10, criterion = 'entropy')
rfc.fit(X_train,y_train)
y_pred = rfc.predict(X_test)
cm = confusion_matrix(y_test,y_pred)
print('RFC')
print(cm)
# 7. ROC , TPR, FPR değerleri
y_proba = rfc.predict_proba(X_test)
print(y_test)
print(y_proba[:,0])
from sklearn import metrics
fpr , tpr , thold = metrics.roc_curve(y_test,y_proba[:,0],pos_label='e')
print(fpr)
print(tpr)
