Çanta Dışı (OOB) Değerlendirme

Python ile Ağaç Tabanlı Modellerle Machine Learning

Elie Kawerk

Data Scientist

Bagging

bazı örnekler bir model için birden çok kez örneklenebilir,
bazı örnekler hiç örneklenmeyebilir.

Çanta Dışı (OOB) örnekler

Ortalama olarak, her model için eğitim örneklerinin %63'ü örneklenir.
Kalan %37 OOB örnekleri oluşturur.

OOB Değerlendirme

oob-degerlendirme

sklearn'de OOB Değerlendirme (Meme Kanseri Veri Kümesi)

# Import models and split utility function
from sklearn.ensemble import BaggingClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split

# Set seed for reproducibility
SEED = 1

# Split data into 70% train and 30% test
X_train, X_test, y_train, y_test =  train_test_split(X, y, test_size= 0.3,
                                                     stratify= y,
                                                     random_state=SEED)

# Instantiate a classification-tree 'dt'
dt = DecisionTreeClassifier(max_depth=4,
                            min_samples_leaf=0.16,
                            random_state=SEED)

# Instantiate a BaggingClassifier 'bc'; set oob_score = True 
bc = BaggingClassifier(base_estimator=dt, n_estimators=300,
                       oob_score=True, n_jobs=-1)


# Fit 'bc' to the training set
bc.fit(X_train, y_train)

# Predict the test set labels
y_pred = bc.predict(X_test)

# Evaluate test set accuracy
test_accuracy = accuracy_score(y_test, y_pred)

# Extract the OOB accuracy from 'bc'
oob_accuracy = bc.oob_score_

# Print test set accuracy
print('Test set accuracy: {:.3f}'.format(test_accuracy))

Test set accuracy: 0.936

# Print OOB accuracy
print('OOB accuracy: {:.3f}'.format(oob_accuracy))

OOB accuracy: 0.925

Hadi pratik yapalım!

Python ile Ağaç Tabanlı Modellerle Machine Learning