python - 具有混合类型特征的 scikit learn 分类器返回 0% 的测试数据准确率

问题描述

我是机器学习和 python 的新手。我想使用 sklearn 中的 DecisionTreeClassifier。由于我的特征部分是数字的，部分是分类的，我需要对它们进行转换，因为 DecisionTreeClassifier 只接受数字特征作为输入。为此，我使用了 ColumnTransformer 和管道。思路如下：

分类和数值特征在单独的管道中进行转换
两者结合形成分类器的输入

但是，使用我的测试数据的准确率始终为 0%，而我使用训练数据的准确率约为 85%。此外，调用 cross_val_score() 返回

ValueError: Found unknown categories ['Holand-Netherlands'] in column 7 during transform

这很奇怪，因为我使用这些数据来训练 full_pipeline。使用不同的分类器会导致相同的行为，这使我相信转换存在问题。非常感谢您的帮助！

下面是我的代码：

names = ["age",
         "workclass",
         "final-weight",
         "education",
         "education-num",
         "martial-status",
         "occupation",
         "relationship",
         "race",
         "sex",
         "capital-gain",
         "capial-loss",
         "hours-per-week",
         "native-country",
         "agrossincome"]

categorical_features = ["workclass", "education", "martial-status", "occupation", "relationship", "race", "sex", "native-country"]
numerical_features = ["age","final-weight", "education-num", "capital-gain", "capial-loss", "hours-per-week"] 
features = np.concatenate([categorical_features, numerical_features])


# create pandas dataframe for adult dataset
adult_train = pd.read_csv(filepath_or_buffer= "https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data" ,
            delimiter= ',',
            index_col = False,
            skipinitialspace = True,
            header = None,
            names = names )

adult_test = pd.read_csv( filepath_or_buffer= "https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test" ,
            delimiter= ',',
            index_col = False,
            skipinitialspace = True,
            header = None,
            names = names )

adult_test.drop(0, inplace =True)
adult_test.reset_index(inplace = True)
adult_train.replace(to_replace= "?", value = np.NaN, inplace = True)
adult_test.replace(to_replace= "?", value = np.NaN, inplace= True)


# split data into features and targets
x_train = adult_train[features]
y_train = adult_train.agrossincome

x_test = adult_test[features]
y_test = adult_test.agrossincome


# create pipeline for preprocessing + classifier
categorical_pipeline = Pipeline( steps = [ ( 'imputer', SimpleImputer(strategy='constant', fill_value='missing') ),
                                           ( 'encoding', OrdinalEncoder() ) 
                                         ])

numerical_pipeline = Pipeline( steps = [ ( 'imputer', SimpleImputer(strategy='median') ),
                                         ( 'std_scaler', StandardScaler( with_mean = False ) ) 
                                       ])

preprocessing = ColumnTransformer( transformers = [ ( 'categorical_pipeline', categorical_pipeline, categorical_features ), 
                                                   ( 'numerical_pipeline', numerical_pipeline, numerical_features ) ] )

full_pipeline = Pipeline(steps= [ ('preprocessing', preprocessing),
                                  ('model', DecisionTreeClassifier(random_state= 0, max_depth = 5) ) ])

full_pipeline.fit(x_train, y_train)
print(full_pipeline.score(x_test, y_test))
#print(cross_val_score(full_pipeline, x_train, y_train, cv=3).mean())

标签： pythonpandasmachine-learningscikit-learnpipeline

python - 具有混合类型特征的 scikit learn 分类器返回 0% 的测试数据准确率

问题描述

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