Chris Setzkorn

using monotonicity constraints - XGBoost as a built-in algorithm

3

Let is assume I have a dataset with 3 independent valiables/predictors/features. So far I used code along those lines successfully: ``` hyperparameters = { 'max_depth': '10', 'num_round': '100', 'objective': 'count:poisson'...

Hi all, I followed the script mode example [here](https://github.com/aws/amazon-sagemaker-examples/blob/c391082711d4297d157e3a546be29ef422a5974b/sagemaker-script-mode/sagemaker-script-mode.ipynb ) , specificaly [this](https://github.com/aws/amazon-sagemaker-examples/blob/c391082711d4297d157e3a546be29ef422a5974b/sagemaker-script-mode/xgboost_script/train_deploy_xgboost_with_dependencies.py). This is great and the cross_validation function [here](https://github.com/aws/amazon-sagemaker-examples/blob/c391082711d4297d157e3a546be29ef422a5974b/sagemaker-script-mode/my_custom_library/cross_validation_xgboost.py) returns errors and the model: ``` def cross_validation(df, K,...

Hi all, I followed the script mode example [here](https://github.com/aws/amazon-sagemaker-examples/blob/c391082711d4297d157e3a546be29ef422a5974b/sagemaker-script-mode/sagemaker-script-mode.ipynb ) , specificaly [this](https://github.com/aws/amazon-sagemaker-examples/blob/c391082711d4297d157e3a546be29ef422a5974b/sagemaker-script-mode/xgboost_script/train_deploy_xgboost_with_dependencies.py). The model fitted model is fitted like this: ``` model_location = args.model_dir + "/xgboost-model" pickle.dump(model, open(model_location, "wb"))...

Constructor failed for class org.apache.spark.api.csharp.CSharpR DD when called with 9 parameters

8

I am using this code: var sparkContext = new SparkContext(new SparkConf().SetAppName("ExcitingWarAndPeaceCount")); var file = sparkContext.TextFile("file:///C:/war_and_peace.txt"); var warsCount = file.Filter(line => line.Contains("war")).Count(); var peaceCount = file.Filter(line => line.Contains("peace")).Count(); see also: http://stackoverflow.com/questions/43449696/run-mobius-word-count-locally-in-debug-mode...

Aci Deployment failed with exception: Error in entry script

6

My use case is to deploy a locally trained model - perfectly valid use case AFIK. For reproducibility I trained this simple model: ```r library(datasets) library(caret) setwd("C:/Data") index

Could this be secured in Azure and if so how please?

I tried to understand the source code but struggle sorry. Let use say my (geo)pandas df has 2 stats: S1 + S2 does the application of the weights depend on...

entity embedding for categorical features with large cardinality

3

I think this would be useful. I would know how to do this using TF + Keras. Not sure if this could be used?