我目前正在根据Tensorflow的’wide’教程使用Tensorflow实现一个逻辑回归器： https://www.tensorflow.org/tutorials/wide
我的代码几乎与教程完全匹配，然而，当我在模型上运行predict()时，它每次都猜测负类，这占了数据的约77%。我如何才能让我的模型尝试一些正向的猜测？我没有进行正则化，因此方差应该最大化。文档声称准确率为84%，而我使用的是完全相同的数据集。可能出了什么问题？这是训练代码：

def train_logistic_model(training_path, response, predictors, num_labels):    # Get csv    df_train = pd.read_csv(training_path, header=0)    # Sanitize column names    unsanitized_column_names = df_train.columns.values    column_names = []    for col in unsanitized_column_names:        column_names.append(re.sub('[^A-Za-z0-9]+', '', col))    # Update dataframe with sanitized column names    df_train = pd.read_csv(training_path, names=column_names, skiprows=1)    # Slice off %10 of training data to test with    df_test = df_train.loc[(len(df_train.index) * .9):]    df_train = df_train.loc[:(len(df_train.index) * .9)]    response_name = column_names[response]    LABEL_COLUMN = "label"    df_train[LABEL_COLUMN] = (df_train[response_name].apply(lambda x: ">50K" in x)).astype(int)    df_test[LABEL_COLUMN] = (df_test[response_name].apply(lambda x: ">50K" in x)).astype(int)    del df_train[response_name]    del df_test[response_name]    # remove NaN elements    df_train = df_train.dropna(how='any', axis=0)    df_test = df_test.dropna(how='any', axis=0)    CATEGORICAL_COLUMNS = []    CONTINUOUS_COLUMNS = []    for key, value in predictors.items():        if value == 'Categorical':            CATEGORICAL_COLUMNS.append(column_names[key])        elif value == 'Continuous':            CONTINUOUS_COLUMNS.append(column_names[key])    # Input bulder function    def input_fn(df):        continuous_cols = {k: tf.constant(df[k].values) for k in CONTINUOUS_COLUMNS}        categorical_cols = {            k: tf.SparseTensor(                indices=[[i, 0] for i in range(df[k].size)],                values=df[k].values,                dense_shape=[df[k].size, 1]) for k in CATEGORICAL_COLUMNS            }        # Merges the two dictionaries into one.        feature_cols = {**continuous_cols, **categorical_cols}        label = tf.constant(df[LABEL_COLUMN].values)        return feature_cols, label    def train_input_fn():        return input_fn(df_train)    def eval_input_fn_test():        return input_fn(df_test)    cat_tensors = []    for col in CATEGORICAL_COLUMNS:        cat_tensors.append(tf.contrib.layers.sparse_column_with_hash_bucket(            column_name=col, hash_bucket_size=100))    cont_tensors = []    for cont in CONTINUOUS_COLUMNS:        cont_tensors.append(tf.contrib.layers.real_valued_column(cont))    feature_columns = cat_tensors + cont_tensors    model_dir = tempfile.mkdtemp()    logistic_model = tf.contrib.learn.LinearClassifier(feature_columns=feature_columns, n_classes=num_labels, model_dir=model_dir)    logistic_model.fit(input_fn=train_input_fn, steps=200)    # Test the model on reserve data    eval_result_test = logistic_model.evaluate(input_fn=eval_input_fn_test, steps=1)    # Test the model on training data    eval_result_train = logistic_model.evaluate(input_fn=eval_input_fn_train, steps=1)    for key in sorted(eval_result_train):        print("%s: %s" % (key, eval_result_train[key]))    return eval_result_test, model_dir

回答：

我想，你需要添加交叉列来让我们的线性模型表现得更好。