Pemusatan dan penskalaan

Supervised Learning dengan scikit-learn

George Boorman

Core Curriculum Manager

Mengapa melakukan penskalaan data?

print(music_df[["duration_ms", "loudness", "speechiness"]].describe())

         duration_ms     loudness       speechiness
count    1.000000e+03    1000.000000    1000.000000
mean     2.176493e+05    -8.284354      0.078642
std      1.137703e+05    5.065447       0.088291
min      -1.000000e+00   -38.718000     0.023400
25%      1.831070e+05    -9.658500      0.033700
50%      2.176493e+05    -7.033500      0.045000
75%      2.564468e+05    -5.034000      0.078642
max      1.617333e+06    -0.883000      0.710000
Supervised Learning dengan scikit-learn

Mengapa melakukan penskalaan data?

  • Banyak model memakai ukuran jarak

  • Fitur berskala besar bisa terlalu memengaruhi model

  • Contoh: KNN memakai jarak secara eksplisit untuk prediksi

  • Kita ingin fitur pada skala serupa

  • Normalisasi atau standardisasi (skala dan pusatkan)

Supervised Learning dengan scikit-learn

Cara melakukan penskalaan data

  • Kurangi mean lalu bagi dengan varians

    • Semua fitur dipusatkan di nol dan ber-varians satu
    • Ini disebut standardisasi

  • Bisa juga kurangi minimum lalu bagi dengan rentang

    • Minimum nol dan maksimum satu

  • Bisa juga normalisasi agar data dari -1 hingga +1

  • Lihat dokumentasi scikit-learn untuk detailnya

Supervised Learning dengan scikit-learn

Penskalaan di scikit-learn

from sklearn.preprocessing import StandardScaler

X = music_df.drop("genre", axis=1).values
y = music_df["genre"].values

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, 
                                                    random_state=42)

scaler = StandardScaler()

X_train_scaled = scaler.fit_transform(X_train)

X_test_scaled = scaler.transform(X_test)

print(np.mean(X), np.std(X))
print(np.mean(X_train_scaled), np.std(X_train_scaled))

19801.42536120538, 71343.52910125865
2.260817795600319e-17, 1.0
Supervised Learning dengan scikit-learn

Penskalaan dalam pipeline

steps = [('scaler', StandardScaler()),
         ('knn', KNeighborsClassifier(n_neighbors=6))]
pipeline = Pipeline(steps)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, 
                                                    random_state=21)

knn_scaled = pipeline.fit(X_train, y_train)

y_pred = knn_scaled.predict(X_test)

print(knn_scaled.score(X_test, y_test))

0.81
Supervised Learning dengan scikit-learn

Membandingkan kinerja dengan data tanpa skala

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, 
                                                    random_state=21)
knn_unscaled = KNeighborsClassifier(n_neighbors=6).fit(X_train, y_train)
print(knn_unscaled.score(X_test, y_test))

0.53
Supervised Learning dengan scikit-learn

CV dan penskalaan dalam pipeline

from sklearn.model_selection import GridSearchCV
steps = [('scaler', StandardScaler()),
         ('knn', KNeighborsClassifier())]
pipeline = Pipeline(steps)

parameters = {"knn__n_neighbors": np.arange(1, 50)}

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, 
                                                    random_state=21)

cv = GridSearchCV(pipeline, param_grid=parameters)

cv.fit(X_train, y_train)

y_pred = cv.predict(X_test)
Supervised Learning dengan scikit-learn

Memeriksa parameter model

print(cv.best_score_)

0.8199999999999999

print(cv.best_params_)

{'knn__n_neighbors': 12}
Supervised Learning dengan scikit-learn

Ayo berlatih!

Supervised Learning dengan scikit-learn

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