Is it possible to generate prediction intervals for time series forecasts when using a Azure AutoML trained models? Could we get the training errors out of the process and use them for bootstrapping?
You can generate forecast quantiles. See the following notebook for more details: https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/forecasting-forecast-function/auto-ml-forecasting-function.ipynb
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I am currently training a model using an azure ML pipeline that i build with sdk. I am trying to add cross-validation to my ml step. I have noticed that you can add this in the parameters when you configure the autoML. My dataset consists of 30% label 0 and 70% label 1.
My question is, does azure autoML stratify data when performing the cross-validation? If not i would have to do the split/stratify myself before passing it to autoML.
Auto ML can stratify the data when performing cross-validation. The following procedure needs to be followed to perform cross-validation
Create the workspace resource.
After giving all the details, click on create
Launch the Studio and go to AutoML and click on New Automated ML job
Upload the dataset from here and give the basic details required.
Dataset uploaded with some basic categories
After uploading dataset use that dataset for the prediction model performance
Here for prediction, we can choose the k-fold cross validation for validation type and number of cross validations as 5. There is no split we are performing. The model will perform according to the validation requirements.
I want to train the model with binary logistic regression model,with a dataset of 3000 data points. while creating the pipeline , it fails at the training model step.
Please help me in training the model with large dataset or retrain the model continuously.
Also Do pipelines have any limitation on the dataset? if so, what is the limit
I haven't seen there is a limitation for training dataset size. May I know how you do the pipeline? If you are using Azure Machine Learning Designer, could you please try the enterprise version? https://learn.microsoft.com/en-us/azure/machine-learning/concept-ml-pipelines#building-pipelines-with-the-designer
Also, I have attached a tutorial here for large data pipeline: https://learn.microsoft.com/en-us/azure/machine-learning/tutorial-pipeline-batch-scoring-classification
I'm trying to work out how to conduct true ARIMA out of sample forecasting in Python. I've been googling for DAYS and there doesn't seem to be an answer.
Say I have a dataset of 75 integers. I'm splitting this dataset 70/30 (train/test) as I would do with any ML model, I'm training the model on train, testing against test, but after training and testing I want to predict values 76-80 which do not currently exist. How is this done? Using the build in predict and forecast function beyond the initial dataset throws back errors because my existing dataset isn't long enough.
Do I need to add artificial data to the dataset to enable this? A point in the right direction would be fantastic.
Apologies for the formatting - submitted via phone.
I am trying to replicate the hyperparameter tuning example reported at this link but I want to use scikit learn XGBoost instead of tensorflow in my training application.
I am able to run multiple trials in a single job, on for each of the hyperparameters combination. However, the Training output object returned by ML-Engine does not include the finalMetric field, reporting metric information (see the differences in the picture below).
What I get with the example of the link above:
Training output object with Tensorflow training app
What I get running my Training application with XGBoost:
Training output object with XGBoost training app
Is there a way for XGBoost to return training metrics to ML-Engine?
It seems that this process is automated for tensorflow, as specified in the documentation:
How Cloud ML Engine gets your metric
You may notice that there are no instructions in this documentation
for passing your hyperparameter metric to the Cloud ML Engine training
service. That's because the service monitors TensorFlow summary events
generated by your training application and retrieves the metric.
Is there a similar mechanism for XGBoost?
Now, I can always dump each metric results to a file at the end of each trial and then analyze them manually to select the best parameters. But, by doing so, am I loosing the automated mechanism offered by Cloud ML Engine, especially concerning the "ALGORITHM_UNSPECIFIED" hyperparameters search algorithm?
i.e.,
ALGORITHM_UNSPECIFIED: [...] applies Bayesian optimization to search
the space of possible hyperparameter values, resulting in the most
effective technique for your set of hyperparameters.
Hyperparameter tuning support of XGBoost was implemented in a different way. We created the cloudml-hypertune python package to help do it. We're still working on the public doc for it. At the meantime, you can refer to this staging sample to learn about how to use it.
Sara Robinson over at google put together a good post on how to do this. Rather than regurgitate and claim it as my own, I'll post this here for anyone else that comes across this post:
https://sararobinson.dev/2019/09/12/hyperparameter-tuning-xgboost.html
I am creating Azure ML experienment to predict multiple values. but in azure ml we can not train a model to predict multiple values. my question is how to bring multiple trained models in single experienment and create webout put that gives me multiple prediction.
You would need to manually save the trained models (right click the module output and save to your workspace) from your training experiment and then manually create the predictive experiment unlike what is done in this document. https://learn.microsoft.com/en-us/azure/machine-learning/studio/walkthrough-5-publish-web-service
Regards,
Jaya