Memilih fitur untuk kinerja model

Pengurangan Dimensi dengan Python

Jeroen Boeye

Head of Machine Learning, Faktion

Contoh dataset Ansur

contoh ansur

Pra-pemrosesan data

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)


from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()

X_train_std = scaler.fit_transform(X_train)

Membangun model regresi logistik

from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

lr = LogisticRegression()
lr.fit(X_train_std, y_train)


X_test_std = scaler.transform(X_test)


y_pred = lr.predict(X_test_std)
print(accuracy_score(y_test, y_pred))

0.99

Meninjau koefisien fitur

print(lr.coef_)

array([[-3.  ,  0.14,  7.46,  1.22,  0.87]])

print(dict(zip(X.columns, abs(lr.coef_[0]))))

{'chestdepth': 3.0, 
 'handlength': 0.14, 
 'neckcircumference': 7.46, 
 'shoulderlength': 1.22, 
 'earlength': 0.87}

Fitur yang sedikit berkontribusi pada model

X.drop('handlength', axis=1, inplace=True)


X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)

lr.fit(scaler.fit_transform(X_train), y_train)

print(accuracy_score(y_test, lr.predict(scaler.transform(X_test))))

0.99

Recursive Feature Elimination

from sklearn.feature_selection import RFE

rfe = RFE(estimator=LogisticRegression(), n_features_to_select=2, verbose=1)

rfe.fit(X_train_std, y_train)

Fitting estimator with 5 features.
Fitting estimator with 4 features.
Fitting estimator with 3 features.

Menghapus satu fitur memengaruhi koefisien fitur lain

Meninjau hasil RFE

X.columns[rfe.support_]

Index(['chestdepth', 'neckcircumference'], dtype='object')

print(dict(zip(X.columns, rfe.ranking_)))

{'chestdepth': 1, 
 'handlength': 4, 
 'neckcircumference': 1, 
 'shoulderlength': 2, 
 'earlength': 3}

print(accuracy_score(y_test, rfe.predict(X_test_std)))

0.99

Ayo berlatih!

Pengurangan Dimensi dengan Python