How to save and load TensorFlow decision forest regression model for incremental learning

import pandas as pd

Read the data

file_path = ‘/content/finaldata3.csv’
df = pd.read_csv(file_path)

df

columns_to_delete = [‘Unnamed: 8’]

df.drop(columns=columns_to_delete, inplace=True)

X = df[[‘Material_Id’, ‘Vendor_Id’]]
y = df[‘Lead_Time’]

y = pd.to_numeric(y)

dataset = tfdf.keras.pd_dataframe_to_tf_dataset(df, label=“Lead_Time”, task=tfdf.keras.Task.REGRESSION)

model = tfdf.keras.RandomForestModel(task=tfdf.keras.Task.REGRESSION)
model.compile(metrics=[“mse”])
model.fit(dataset)

import pickle

with open(“/content/trained_model2.pkl”, “wb”) as f:
pickle.dump(model, f)

with open(“/content/trained_model2.pkl”, “rb”) as f:
pretrained_model = pickle.load(f)

new_data_file_path = ‘/content/mat1ven3.csv’
new_data_df = pd.read_csv(new_data_file_path)

X_new = new_data_df[[‘Material_Id’, ‘Vendor_Id’]]
y_new = new_data_df[‘Lead_Time’]

y_new = pd.to_numeric(y_new)

new_dataset = tfdf.keras.pd_dataframe_to_tf_dataset(new_data_df, label=“Lead_Time”, task=tfdf.keras.Task.REGRESSION)

pretrained_model.compile(metrics=[“mse”])

pretrained_model.fit(new_dataset)

I am getting the error as
ValueError: The model’s task attribute (CLASSIFICATION) does not match the task attribute passed to pd_dataframe_to_tf_dataset (REGRESSION).