含遺漏值或變異小的特徵

Python 的降維

Jeroen Boeye

Head of Machine Learning, Faktion

建立特徵選擇器

print(ansur_df.shape)

(6068, 94)

from sklearn.feature_selection import VarianceThreshold

sel = VarianceThreshold(threshold=1)

sel.fit(ansur_df)

mask = sel.get_support()
print(mask)

array([ True,  True, ..., False,  True])

套用特徵選擇器

print(ansur_df.shape)

(6068, 94)

reduced_df = ansur_df.loc[:, mask]
print(reduced_df.shape)

(6068, 93)

變異選擇器的注意事項

buttock_df.boxplot()

特徵箱型圖

標準化變異量

from sklearn.feature_selection import VarianceThreshold

sel = VarianceThreshold(threshold=0.005)

sel.fit(ansur_df / ansur_df.mean())

mask = sel.get_support()
reduced_df = ansur_df.loc[:, mask]
print(reduced_df.shape)

(6068, 45)

遺漏值選擇器

寶可夢範例

遺漏值選擇器

寶可夢範例含 NaN

辨識遺漏值

pokemon_df.isna()

寶可夢遺漏值布林示意

計算遺漏值數量

pokemon_df.isna().sum()

Name         0
Type 1       0
Type 2     386
Total        0
HP           0
Attack       0
Defense      0
dtype: int64

計算遺漏值比例

pokemon_df.isna().sum() / len(pokemon_df)

Name       0.00
Type 1     0.00
Type 2     0.48
Total      0.00
HP         0.00
Attack     0.00
Defense    0.00
dtype: float64

套用遺漏值門檻

# Fewer than 30% missing values = True value
mask = pokemon_df.isna().sum() / len(pokemon_df) < 0.3
print(mask)

Name        True
Type 1      True
Type 2     False
Total       True
HP          True
Attack      True
Defense     True
dtype: bool

套用遺漏值門檻

reduced_df = pokemon_df.loc[:, mask]

reduced_df.head()

套用遮罩後的寶可夢範例

一起來練習吧！

Python 的降維