ValueError: y contains previously unseen labels: ‘103125‘

解决ValueError: y contains previously unseen labels: '103125'

引发原因：有些标签训练集不存在，但却在测试集出现了，而且我们LabelEncoder使用的拟合fit是训练集的数据

解决方式：把原数据集里面没有但是在新数据集遇到的新值放到一个类里面，再将类回传给LabelEncoder. 没有在训练集中出现的label，均视为"unknown"

报错代码块：

更改图中红色框内部分：

def transform_inputs(df, real_scalers, categorical_scalers, real_columns, categorical_columns):"""特征处理transform部分"""out = df.copy()out[real_columns] = real_scalers.transform(df[real_columns].values)for col in categorical_columns:string_df = df[col].apply(str)# error处理string_df = string_df.map(lambda s:'<unknown>' if s not in categorical_scalers[col].classes_ else s)categorical_scalers[col].classes_ = np.append(categorical_scalers[col].classes_, '<unknown>')out[col] = categorical_scalers[col].transform(string_df)return out

错误解决

ValueError: y contains previously unseen labels: ‘103125‘相关推荐

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主要思路:把原数据集(比如train data set)里面没有但是在新数据集(比如test data set)遇到的新值放到一个类里面,再将类回传给LabelEncoder.
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ValueError: y contains previously unseen labels: ‘103125‘

解决ValueError: y contains previously unseen labels: '103125'

ValueError: y contains previously unseen labels: ‘103125‘相关推荐

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