如何优化汽车价格预测模型的RMSE指标？

1. 基于行驶里程估算补全缺失的condition字段值

new_condition_df = df[df['condition'].map(condition_mapping) == 2]
top_1000_highest_mileage = new_condition_df.nlargest(1000, 'mileage')['mileage']
average_top_1000_highest_mileage = top_1000_highest_mileage.mean()

# 筛选condition字段为空或未指定的行
null_condition_df = df[df['condition'].isnull() | (df['condition'] == '')]

# 根据行驶里程更新condition字段
null_condition_df.loc[null_condition_df['mileage'] >= average_top_1000_highest_mileage, 'condition'] = 'CONDITION_USED'
null_condition_df.loc[null_condition_df['mileage'] < average_top_1000_highest_mileage, 'condition'] = 'CONDITION_NEW'

# 将修改后的行更新回原DataFrame
df.update(null_condition_df)

2. 处理缺失值：删除部分含空值的行，填充特定字段

columns_with_null = ['color', 'vat_reclaimable', 'cubic_capacity', 'seller_country', 'feature']
df.dropna(subset=columns_with_null, inplace=True)

df['air_conditioning'].fillna('AIRCONDITIONING_NONE', inplace=True)
df['parking_camera'].fillna('PARKINGCAMERA_NONE', inplace=True)
df['parking_sensors'].fillna('PARKINGSENZOR_NONE', inplace=True)

3. 针对缺失值较多的drive字段（记录车辆四驱/两驱信息），通过建模估算补全

features = ['mileage', 'cubic_capacity', 'power', 'year'] + list(df.columns[df.columns.str.startswith('car_style_')]) + list(df.columns[df.columns.str.startswith('transmission_')]) + list(df.columns[df.columns.str.startswith('fuel_type_')])

train_data = df.dropna(subset=['drive'])  # 删除drive字段含缺失值的行
X_train, X_test, y_train, y_test = train_test_split(train_data[features], pd.get_dummies(train_data['drive']), test_size=0.2, random_state=42)

model = LinearRegression()
model.fit(X_train, y_train)

missing_data = df[df['drive'].isnull()]
X_missing = missing_data[features]
predicted_values = model.predict(X_missing)


df_imputed = df.copy()
predicted_df = pd.DataFrame(predicted_values, columns=y_train.columns, index=missing_data.index)
df_imputed.loc[df_imputed['drive'].isnull(), y_train.columns] = predicted_df.values

predicted_df_encoded = pd.DataFrame(predicted_values, columns=y_train.columns, index=missing_data.index)
predicted_df_encoded = (predicted_df_encoded > 0.5).astype(int)

for column in predicted_df_encoded.columns:
    df_imputed[column] = 0  # 添加全为0的字段
    df_imputed.loc[predicted_df_encoded.index, column] = predicted_df_encoded[column].values  

unique_values_imputed_encoded = df_imputed['drive'].unique()
df = df_imputed
df.drop(columns=['drive'], inplace=True)

4. 对feature字段进行多标签编码处理

from sklearn.preprocessing import MultiLabelBinarizer

mlb = MultiLabelBinarizer()
df = df.join(pd.DataFrame(mlb.fit_transform(df['feature']),columns=mlb.classes_))
df.fillna(0, inplace=True)

df = df.drop(columns=['feature'])

5. 模型训练过程

df_encoded = pd.get_dummies(df)

X_train, X_test, y_train, y_test = train_test_split(df_encoded.drop(columns=['price_with_vat_czk']), df_encoded['price_with_vat_czk'], test_size=0.25, random_state=42)

model = LinearRegression()
model.fit(X_train, y_train)

y_pred = model.predict(X_test)

完整代码及数据集可参考对应实现，当前模型的RMSE约为64000。

内容的提问来源于stack exchange，提问作者Aaron7