[머신러닝] Decision Tree

DecisionTreeClassifier로 데이터셋을 여러 갈래로 나눠 분류할수 있다.

 

 

df=

 

 

전처리 과정:

 

X = df.iloc[:, [2,3]]
y = df['Purchased']

from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X = scaler.fit_transform(X)

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.25,random_state=3)

 

 

sklearn.tree 라이브러리의 DecisionTreeClassifier를 사용한다.

 

from sklearn.tree import DecisionTreeClassifier
classifier = DecisionTreeClassifier(random_state=3)
classifier.fit(X_train,y_train)

 

 

성능평가:

 

in:

y_pred = classifier.predict(X_test)
from sklearn.metrics import accuracy_score
accuracy_score(y_test,y_pred)

out:

0.81

# 정확도는 81%다.

 

 

차트로 표현하면:

 

in:

# Visualising the Test set results
from matplotlib.colors import ListedColormap
X_set, y_set = X_test, y_test
X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
plt.figure(figsize=[10,7])
plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
             alpha = 0.75, cmap = ListedColormap(('red', 'green')))
plt.xlim(X1.min(), X1.max())
plt.ylim(X2.min(), X2.max())
for i, j in enumerate(np.unique(y_set)):
    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
                c = ListedColormap(('red', 'green'))(i), label = j)
plt.title('Classifier (Test set)')
plt.xlabel('Age')
plt.ylabel('Estimated Salary')
plt.legend()
plt.show()

out:

# 가로세로로 블럭형태로 나눠지는것을 볼수있다.