Issue with Gradient Boosting Tree Regressor

I'm currently trying to run a GradientBoostedTree model in a python code recipie as shown below:

ipykernel=4.8.2=py37_0
ipython=7.29.0=py37hb070fc8_0
ipython_genutils=0.2.0=pyhd3eb1b0_1
ipywidgets=7.6.5=pyhd3eb1b0_1
jupyter=1.0.0=py37_7
jupyter_client=5.2.4=py37_0
jupyter_console=6.4.0=pyhd3eb1b0_0
jupyter_core=4.9.1=py37h06a4308_0
jupyterlab_widgets=1.0.0=pyhd3eb1b0_1
numpy=1.21.2=py37h20f2e39_0
numpy-base=1.21.2=py37h79a1101_0
pandas=1.0.5=py37h0573a6f_0
python=3.7.11=h12debd9_0
scikit-learn=1.0.1=py37h51133e4_0
xgboost=0.82=py37h1aa3f02_0

However, when I run this, I get the error in the attached picture.

I have the following installed:

# -*- coding: utf-8 -*-
import dataiku
import pandas as pd, numpy as np
from dataiku import pandasutils as pdu
from sklearn.ensemble import GradientBoostingRegressor

# Read recipe inputs
field = dataiku.Dataset("HM_Analysis_FieldLevel_prepared")
field_df = field.get_dataframe()
# GBT_81
model_1 = dataiku.Model("WyoWL843")
pred_1 = model_1.get_predictor()

# GBT_79
model_2 = dataiku.Model("sXriI8AV")
pred_2 = model_2.get_predictor()

data = field_df['Total_diff_sum_sum']<=250
data.head()

# Compute recipe outputs from inputs
# TODO: Replace this part by your actual code that computes the output, as a Pandas dataframe
# NB: DSS also supports other kinds of APIs for reading and writing data. Please see doc.

best_fit_df = field_df # For this sample code, simply copy input to output

# Write recipe outputs
best_fit = dataiku.Dataset("best_fit")
best_fit.write_with_schema(best_fit_df)

What am I doing wrong?