Day 23 - Cupoy

2019/05/10 01:34 上午

機器學習共學討論版

Patrick Liou

觀看數：5

回答數：3

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只取類別值

對照組

ml100-2

ml100-2-d23

#只取類別值 (object) 型欄位, 存於 object_features 中

object_features = []

for dtype, feature in zip(df.dtypes, df.columns):

    if dtype == 'object':

        object_features.append(feature)

print(f'{len(object_features)} Numeric Features : {object_features}\n')

# 只留類別型欄位

df = df[object_features]

df = df.fillna('None')

train_num = train_Y.shape[0] ---> 不太了解此行的用意

df.head()

# 對照組 : 標籤編碼 + 邏輯斯迴歸

df_temp = pd.DataFrame()

for c in df.columns:

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

train_X = df_temp[:train_num]---> 為什麼要加入 [:train_num]，直接寫成df_temp 會有差異嗎?

estimator = LogisticRegression()

start = time.time()

print(f'shape : {train_X.shape}')

print(f'score : {cross_val_score(estimator, train_X, train_Y, cv=5).mean()}')

print(f'time : {time.time() - start} sec')