首先，我按照教程中編碼的示例進(jìn)行操作并創(chuàng)建以下代碼：import numpy as npimport pandas as pdimport tensorflow as tffrom tensorflow import feature_columnfrom tensorflow.keras import layersfrom sklearn.model_selection import train_test_splitURL = 'https://storage.googleapis.com/applied-dl/heart.csv'dataframe = pd.read_csv(URL)dataframe.head()train, test = train_test_split(dataframe, test_size=0.2)train, val = train_test_split(train, test_size=0.2)def df_to_dataset(dataframe, shuffle=True, batch_size=32):  dataframe = dataframe.copy()  labels = dataframe.pop('target')  ds = tf.data.Dataset.from_tensor_slices((dict(dataframe), labels))  if shuffle:    ds = ds.shuffle(buffer_size=len(dataframe))  ds = ds.batch(batch_size)  return dsbatch_size = 32train_ds = df_to_dataset(train, batch_size=batch_size)val_ds = df_to_dataset(val, shuffle=False, batch_size=batch_size)test_ds = df_to_dataset(test, shuffle=False, batch_size=batch_size)feature_columns = []age = feature_column.numeric_column("age")# numeric colsfor header in ['age', 'trestbps', 'chol', 'thalach', 'oldpeak', 'slope', 'ca']:  feature_columns.append(feature_column.numeric_column(header))# bucketized colsage_buckets = feature_column.bucketized_column(age, boundaries=[18, 25, 30, 35, 40, 45, 50, 55, 60, 65])feature_columns.append(age_buckets)# indicator colsthal = feature_column.categorical_column_with_vocabulary_list(      'thal', ['fixed', 'normal', 'reversible'])thal_one_hot = feature_column.indicator_column(thal)feature_columns.append(thal_one_hot)# embedding colsthal_embedding = feature_column.embedding_column(thal, dimension=8)feature_columns.append(thal_embedding)我的問(wèn)題是我應(yīng)該使用什么方法將浮點(diǎn)數(shù)結(jié)果轉(zhuǎn)換為二進(jìn)制（0或1）并比較目標(biāo)？我的最終目標(biāo)是獲得通過(guò)評(píng)估方法獲得的精度值0.6885246。