Bagging

Maskininlärning med trädbaserade modeller i Python

Elie Kawerk

Data Scientist

Ensemblemetoder

Voting Classifier

samma träningsuppsättning,
$\neq$ algoritmer.

Bagging

en algoritm,
$\neq$ delmängder av träningsuppsättningen.

Bagging

Bagging: Bootstrap Aggregation.
Använder en teknik kallad bootstrap.
Minskar variansen hos enskilda modeller i ensemblen.

Bootstrap

bootstrap

Bagging: Träning

träning-bagg

Bagging: Prediktion

prediktion-bagg

Bagging: Klassificering & Regression

Klassificering:

Aggregerar prediktioner med majoritetsröstning.
BaggingClassifier i scikit-learn.

Regression:

Aggregerar prediktioner genom medelvärdesberäkning.
BaggingRegressor i scikit-learn.

BaggingClassifier i sklearn (bröstcancerdatamängd)

# Import models and utility functions
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'
bc = BaggingClassifier(base_estimator=dt, n_estimators=300, n_jobs=-1)

# Fit 'bc' to the training set
bc.fit(X_train, y_train)
# Predict test set labels
y_pred = bc.predict(X_test)

# Evaluate and print test-set accuracy
accuracy = accuracy_score(y_test, y_pred)
print('Accuracy of Bagging Classifier: {:.3f}'.format(accuracy))

Accuracy of Bagging Classifier: 0.936

Nu kör vi en övning!

Maskininlärning med trädbaserade modeller i Python