[Bug Report] Xgboost in Sagemaker gives wildly different (and incorrect) result from my local model

**Describe the bug**
I trained a binary classification model with Sagemaker's container for xgboost 1.7-1.
I also have previously developed a xgboost model for the same dataset locally.
The positive rate for the dataset is generally < 4%, very low occurrence.

When I compared the predicted probabilities from the sagemaker builtin model and my local model, the results are opposite.
Given the low positive rate I believe the sagemaker model outputs are incorrect.
See images.

I checked the inputs for training and they are identical, except that on my local machine I fed the model csv file whereas for sagemaker xgboost it required the data in libsvm format. But after double checking the training data were the same. I also fed the same hyperparameter.

**To reproduce**
For sagemaker:
```
from sagemaker.xgboost.estimator import XGBoost
# version 1:
xgb_script_mode_estimator = XGBoost(
entry_point=script_path,
framework_version="1.7-1",
# hyperparameters=hyperparameters,
role=role,
instance_count=2,
instance_type=instance_type,
output_path=output_path,
code_location=output_path
)
# calling fit

# version 2:
from sagemaker.amazon.amazon_estimator import get_image_uri
container = get_image_uri(boto3.Session().region_name, "xgboost", "1.7-1")

xgb = sagemaker.estimator.Estimator(
container,
role,
instance_count=1,
instance_type="ml.m4.xlarge",
output_path="s3://{}/{}/output".format(s3_bucket, key, "no-show-xgb"),
sagemaker_session=sess,
)
# calling fit
```

Is there any way to debug this issue?

Start by reproducing the two SageMaker configurations shown in the report, using the same training data, hyperparameters, and model version. Compare the local CSV path with the SageMaker libsvm input and the differing estimator settings. Done means identifying a reproducible cause for the prediction mismatch or documenting evidence that the issue cannot be reproduced.

Written by the indexing model from the issue text.