Aplikasi PCA

Pengurangan Dimensi dengan Python

Jeroen Boeye

Head of Machine Learning, Faktion

Memahami komponen

print(pca.components_)

array([[  0.71, 0.71],
       [ -0.71, 0.71]])

PC 1 = 0.71 x Panjang tangan + 0.71 x Panjang kaki

PC 2 = -0.71 x Panjang tangan + 0.71 x Panjang kaki

panjang tangan vs kaki dengan vektor

PCA untuk eksplorasi data

Komponen dengan kelas tinggi badan

PCA dalam pipeline

from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.pipeline import Pipeline

pipe = Pipeline([
        ('scaler', StandardScaler()),
        ('reducer', PCA())])

pc = pipe.fit_transform(ansur_df)

print(pc[:,:2])

array([[-3.46114925,  1.5785215 ],
       [ 0.90860615,  2.02379935],
       ...,
       [10.7569818 , -1.40222755],
       [ 7.64802025,  1.07406209]])

Memeriksa efek fitur kategorikal

print(ansur_categories.head())

   Branch                  Component     Gender  BMI_class   Height_class
0  Combat Arms             Regular Army  Male    Overweight  Tall
1  Combat Support          Regular Army  Male    Overweight  Normal
2  Combat Support          Regular Army  Male    Overweight  Normal
3  Combat Service Support  Regular Army  Male    Overweight  Normal
4  Combat Service Support  Regular Army  Male    Overweight  Tall

Memeriksa efek fitur kategorikal

ansur_categories['PC 1'] = pc[:,0]
ansur_categories['PC 2'] = pc[:,1]

sns.scatterplot(data=ansur_categories, 
                x='PC 1', y='PC 2', 
                hue='Height_class', alpha=0.4)

Komponen dengan kelas tinggi badan

Memeriksa efek fitur kategorikal

sns.scatterplot(data=ansur_categories, 
                x='PC 1', y='PC 2', 
                hue='Gender', alpha=0.4)

Komponen dengan kelas gender

Memeriksa efek fitur kategorikal

sns.scatterplot(data=ansur_categories, 
                x='PC 1', y='PC 2', 
                hue='BMI_class', alpha=0.4)

Komponen dengan kelas IMT

PCA dalam pipeline model

pipe = Pipeline([
        ('scaler', StandardScaler()),
        ('reducer', PCA(n_components=3)),
        ('classifier', RandomForestClassifier())])

print(pipe['reducer'])

PCA(n_components=3)

PCA dalam pipeline model

pipe.fit(X_train, y_train)

pipe['reducer'].explained_variance_ratio_

array([0.56, 0.13, 0.05])

pipe['reducer'].explained_variance_ratio_.sum()

0.74

print(pipe.score(X_test, y_test))

0.986

Ayo berlatih!

Pengurangan Dimensi dengan Python