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Suggestions:
* organize examples by interfaces
* add new examples
* different initial design
* optimization on a fixed grid of hyperparameters
* add an examples on the warmstarting …
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Just a head's up that clicking on this link in the tutorial page leads to a 404.
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In #356 we started seeing that we can play with hyperparameters to reduce the runtime while having high accuracy.
Once #321 is resolved, we can start looking into hyperparameter optimization:
- [ …
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# MLB Model Optimization | rdpharr’s projects
Part 4 - Solidly outperforming the casinos after hyperparameter optimization & more data
[https://rdpharr.github.io/project_notes/baseball/hyperopt/xgbo…
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**What happened**:
I tried the example for hyperparameter optimization: https://examples.dask.org/machine-learning/hyperparam-opt.html
**What you expected to happen**:
it to not fail
**M…
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I found the `optimize_restarts` feature from GPy to be particularly useful for getting really good fits especially in the way I am working with GPs. I also noticed that there was work done for this ba…
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When running TPE hyperparameter optimization with
```python
parameters.hyperparameters.hyper_opt_method = "optuna"
parameters.running.after_before_training_metric = "band_energy"
...
```
validat…
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Hey Jordan, looking at problem 2.4, how do you want us to implement the neural network? Do you want us to use:
**Method #1**
model = Sequential()
model.add(Dense(256, activation='relu',input_shap…
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Hello,
I am using surrogates.jl v6.3.0 to build kriging for a dataset with 500 elements. There are 24 input variables and 1 output. But I found the accuracy is terrible. There is serious overfittin…
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Hi,
I believe that higher could be used for hyperparameter optimization using bilevel programming. I have attempted to adapt the given meta-learning example for bilevel programming. However, I am s…