the classProbability of XGBoost is different between python and coreml model

## ❓Question
I had trained a XGBoost model 'bst' and used coremltools to get a CoreML model. And I used the same input but the output is different. How could I do to get the same results ? The code is below:

```
# get coreml model
import coremltools
coremlmodel = coremltools.converters.xgboost.convert(bst, force_32bit_float=False, mode='classifier', n_classes=7)
# predict
bst_result = bst.predict(xgb.DMatrix(X_test.iloc[0:1].values))
dictinput = {'f{}'.format(x): X_test.iloc[0, x] for x in range(X_test.shape[1])}
coreml_result = coremlmodel.predict(dictinput)
# print result
print('bst result: ', bst_result)
print('coreml result', coreml_result)
```
The result is below:
```
bst result: [[0.28688842 0.00184444 0.00135211 0.70432913 0.00201192 0.00176237
0.00181162]]

coreml result {'target': 3, 'classProbability': {0: 1.325793311052, 5: -3.7666387138099995, 1: -3.7211242350999996, 6: -3.7390789153600004, 2: -4.0316371452, 3: 2.223946214053901, 4: -3.634210373570001}}
```

The target of coreml result is right, but the classProbability is not in 0~1. How could I do to make the two results is the same? Thanks!

## System Information
Mac OS: 10.15.3
python version: 3.7.6
XGBoost version: 0.9
coremltools version: 3.4

No repository file or test is named. Start by reproducing the discrepancy with bst.predict and coremltools.converters.xgboost.convert using the reported versions, then inspect the converter's classifier output semantics and compare its values with XGBoost probabilities. Done means identifying the cause and making the two probability outputs equivalent, or documenting why they cannot be.

Written by the indexing model from the issue text.