[百日馬拉松] 機器學習-特徵工程

最近參加了機器學習百日馬拉松的活動，單純記錄下這100天python機器學習中每日覺得最有收穫的地方，如果有想參加這活動的朋友，真心推薦參加！此次機器學習-百日馬拉松的相關代碼放置於：https://github.com/hsuanchi/ML-100-days

相關文章：[百日馬拉松] 機器學習-資料清理https://www.maxlist.xyz/2019/03/03/ml_100days/

Table

一. 標籤編碼 ( Label Encoding )

類似於流⽔號，依序將新出現的類別依序編上新代碼，已出現的類別編上已使⽤的代碼
確實能轉成分數，但缺點是分數的⼤⼩順序沒有意義

df_temp = pd.DataFrame()

for c in df.columns:

df_temp[c] = LabelEncoder().fit_transform(df[c])

二. 獨熱編碼 ( One Hot Encoding )

為了改良數字⼤⼩沒有意義的問題，將不同的類別分別獨立為⼀欄
缺點是需要較⼤的記憶空間與計算時間，且類別數量越多時越嚴重

1	df_temp = pd.get_dummies(df)

三. 均值編碼 (Mean Encoding)

for col in object_features:

print(col)

df_mean = data.groupby([col]['Survived'].mean().reset_index()

df_mean.columns = [col, f"{col}_mean"]

data = pd.merge(data, df_mean, on=col, how='left')

data = data.drop([col], axis=1)

四. 計數編碼 ( Counting )

如果類別的⽬標均價與類別筆數呈正相關 ( 或負相關 )，也可以將筆數本⾝當成特徵

df_count = df.groupby('Cabin')['Name'].agg('size').reset_index() df_count.columns = ['Cabin', 'Cabin_Count'] df = pd.merge(df, df_count, on='Cabin', how='left') df.head()

df_temp = pd.DataFrame() for col in object_features: df_temp[col] = LabelEncoder().fit_transform(df[col]) df_temp['Cabin_Hash'] = df['Cabin'].map(lambda x: hash(x) % 10)

五. 群聚編碼 (Group by Encoding)

數值型特徵對⽂字型特徵最重要的特徵組合⽅式
常⾒的有 mean, median, mode, max, min, count 等

mean_df = df.groupby(['Embarked'])['Fare'].mean().reset_index()

mode_df = df.groupby(['Embarked'])['Fare'].apply(lambda x: x.mode()[0]).reset_index()

median_df = df.groupby(['Embarked']['Fare'].median().reset_index()

max_df = df.groupby(['Embarked'])['Fare'].max().reset_index()

temp = pd.merge(mean_df, mode_df, how='left', on=['Embarked'])

temp = pd.merge(temp, median_df, how='left', on=['Embarked'])

temp = pd.merge(temp, max_df, how='left', on=['Embarked'])

temp.columns = ['Embarked', 'Fare_Mean', 'Fare_Mode', 'Fare_Median', 'Fare_Max']

六. 葉編碼(leaf encoding)

將決策樹的葉點當做新特徵
再用邏輯回歸合併預測

# 隨機森林擬合後

rf = RandomForestClassifier(n_estimators=20, min_samples_split=10, min_samples_leaf=5,

max_features=4, max_depth=3, bootstrap=True)

將葉編碼 (*.apply) 結果做獨熱 / 邏輯斯迴歸

onehot = OneHotEncoder()

lr = LogisticRegression(solver='lbfgs', max_iter=1000)

rf.fit(train_X, train_Y)

onehot.fit(rf.apply(train_X))

lr.fit(onehot.transform(rf.apply(val_X)), val_Y)

七. 特徵雜湊 ( Feature Hash )

特徵雜湊是⼀種折衷⽅案
將類別由雜湊函數定應到⼀組數字
調整雜湊函數對應值的數量
在計算空間/時間與鑑別度間取折衷
也提⾼了訊息密度, 減少無⽤的標籤

df_temp = pd.DataFrame()

for col in object_features:

df_temp[col] = LabelEncoder().fit_transform(df[col])

df_temp['Cabin_Hash'] = df['Cabin'].map(lambda x: hash(x) % 10)

八. 空缺值：

df_m1 = df.fillna(-1)

df_0 = df.fillna(0)

df_md = df.fillna(df.median())

九. 標準化：

from sklearn.preprocessing import MinMaxScaler, StandardScaler

df_mm = MinMaxScaler().fit_transform(df)

df_sc = StandardScaler().fit_transform(df)

十. 離群值：

＃限制離群值在500-2000內

df['GrLivArea'] = df['GrLivArea'].clip(500, 2000)

sns.regplot(x = df['GrLivArea'], y=train_Y)

plt.show()

＃捨棄離群值

keep_indexs = (df['GrLivArea']> 500) & (df['GrLivArea']< 2000)

df = df[keep_indexs]

train_Y = train_Y[keep_indexs]

sns.regplot(x = df['GrLivArea'], y=train_Y)

plt.show()

十一. 去偏態：

1 2	＃原始 sns.distplot(df['Fare'])

# 對數去偏 (log1p)

df_fixed['Fare'] = np.log1p(df_fixed['Fare'])

sns.distplot(df_fixed['Fare'][:train_num])

plt.show()

# 分佈去偏(boxcox)

from scipy import stats

df_fixed['Fare'] = stats.boxcox(df_fixed['Fare']+1)[0]

sns.distplot(df_fixed['Fare'][:train_num])

plt.show()

# ⽅根去偏(sqrt)

df_fixed['Fare'] = stats.boxcox(df['Fare']+1, lmbda=0.5)

sns.distplot(df_fixed['Fare'][:train_num])

plt.show()

十二. 特徵工程流程：

讀取資料
分解重組與轉換
- train+test=df
特徵工程
- Label Encoder
- MinMax Encoder
訓練模型與預測
提交