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1. Use the existing map to label the existing turning points on the map
2. Training model based on the tracks and the labelled turning points
3. Test the trained model on raw tracks.
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Using a multi-class model created with ``.
```
from pycaret.classification import *
import shap
shap.initjs()
experiment = setup(df, 'AGE_GRP', silent=True, session_id=42)
model = create_…
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Hi all,
I am trying to use randomized cross validation on dask-xgboost.
Here is the snippet of code I am trying to get to work:
```
from dask_ml.model_selection import RandomizedSearchCV
from…
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I am using XGBoost Dask to train a regression model.
I use `Optuna` to tune the process to find the best parameter. Once you defined the function `objective`, below is a typical Optuna tuning stru…
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请问一下,这个xgboost的python模型代码有没有.我自己训练的好像不管用.
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After running 20 random initialization rounds, starting at about the 20th round of the algorithm, the elapsed time (printed on the screen) does not match the time I get if I time the code with stop wa…
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Hi, I can see that the custom objective function for the Scala API was recently added in this [PR](https://github.com/microsoft/SynapseML/pull/1054), which is really exciting! Is there any idea when t…
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The current design of ADMM package funtions adopts the fluent style of programming, that is, the operations are done in a pipeline.
This can be friendly to interactive usage of model fitting functi…
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!-- Please include a self-contained copy-pastable example that generates the issue if possible.
Please be concise with code posted. See guidelines below on how to provide a good bug report:
- Cr…
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I want to work on this issue as it hadn't mentioned earlier. I have great knowledge of model selection and it might be of great use here.