import tensorflow_decision_forests as tfdf # Load the training dataset using pandas import pandas train_df = pandas.read_csv("penguins_train.csv")# Convert the pandas dataframe into a TensorFlow dataset train_ds = tfdf.keras.pd_dataframe_to_tf_dataset(train_df, label="species")# Train the...
当馈送一个用tfdf.keras.pd_dataframe_to_tf_dataset()转换的Pandas DataFrame时，权重列可以直接用wei...
我遇到了同样的问题。它通过重命名列来修复，这样里面就没有空格了。tensorflow数据集半自动地用“_"...
# Convert the pandas dataframe into a TensorFlow dataset train_ds = tfdf.keras.pd_dataframe_to_tf_dataset(train_df, label="species") # Train the model model = tfdf.keras.RandomForestModel() model.fit(train_ds) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 请注意,代码中没有提...
train_ds = tfdf.keras.pd_dataframe_to_tf_dataset(train_df, label="species") # Train the model model = tfdf.keras.RandomForestModel() model.fit(train_ds) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 请注意,代码中没有提供输入特性或超参数。这意味着,TensorFlow 决策森林将自动检测...
train_ds=tfdf.keras.pd_dataframe_to_tf_dataset(train_df,label="species")# Train the model model=tfdf.keras.RandomForestModel()model.fit(train_ds) 请注意,代码中没有提供输入特性或超参数。这意味着,TensorFlow 决策森林将自动检测此数据集中的输入特征,并对所有超参数使用默认值。
# Load the testing datasettest_df = pandas.read_csv("penguins_test.csv")# Convert it to a TensorFlow datasettest_ds = tfdf.keras.pd_dataframe_to_tf_dataset(test_df, label="species")# Evaluate the modelmodel.compile(...
training dataset using pandasimport pandastrain_df = pandas.read_csv("penguins_train.csv")# Convert the pandas dataframe into a TensorFlow datasettrain_ds = tfdf.keras.pd_dataframe_to_tf_dataset(train_df, label="species")# Train the modelmodel = tfdf.keras.RandomForestModel()model.fit(...