![]() ![]() # $Chocolate圜entralManagementUrl = " # ii. # If using CCM to manage Chocolatey, add the following: ![]() # This url should result in an immediate download when you navigate to it # $RequestArguments.Credential = $NugetRepositor圜redential # ("password" | ConvertTo-SecureString -AsPlainText -Force) # If required, add the repository access credential here $NugetRepositoryUrl = "INTERNAL REPO URL" # Should be similar to what you see when you browse Your internal repository url (the main one). # We use this variable for future REST calls. ::SecurityProtocol = ::SecurityProtocol -bor 3072 # installed (.NET 4.5 is an in-place upgrade). Description: RapidMiner Studio software is capable topics cover art, machine learning, analyzing and forecasting and analysis of the. Working with RapidMiner Studio Professional 7.1.1 圆4 full license. NET 4.0, even though they are addressable if. Working with RapidMiner Studio Professional 7.1.1 圆4 full license Link download RapidMiner Studio Professional 7.1.1 win64 full cracked. # Use integers because the enumeration value for TLS 1.2 won't exist # Set TLS 1.2 (3072) as that is the minimum required by various up-to-date repositories. # We initialize a few things that are needed by this script - there are no other requirements. # You need to have downloaded the Chocolatey package as well. Download Chocolatey Package and Put on Internal Repository # # repositories and types from one server installation. # are repository servers and will give you the ability to manage multiple # Chocolatey Software recommends Nexus, Artifactory Pro, or ProGet as they # generally really quick to set up and there are quite a few options. # You'll need an internal/private cloud repository you can use. Internal/Private Cloud Repository Set Up # # Here are the requirements necessary to ensure this is successful. Your use of the packages on this site means you understand they are not supported or guaranteed in any way. With any edition of Chocolatey (including the free open source edition), you can host your own packages and cache or internalize existing community packages. Packages offered here are subject to distribution rights, which means they may need to reach out further to the internet to the official locations to download files at runtime.įortunately, distribution rights do not apply for internal use. If you are an organization using Chocolatey, we want your experience to be fully reliable.ĭue to the nature of this publicly offered repository, reliability cannot be guaranteed. Human moderators who give final review and sign off.Security, consistency, and quality checking.ModerationĮvery version of each package undergoes a rigorous moderation process before it goes live that typically includes: Is it the best choice?! Remark: Training Data is more balanced than testing data.Welcome to the Chocolatey Community Package Repository! The packages found in this section of the site are provided, maintained, and moderated by the community. Because of unbalanced data I decided to use AUC. What performance criterion should I use for TRAINING and/or TESTING. I applied it to the original TEST data set to get a performance value.ġ. I build a decision tree on selected features ( minimal leave size: 15, minimal size to split: 100)ĥ. I used several feature selection algorithmsĤ. IN TRAINING DATA I oversampled negative class by factor 20 (bootsstrap sample) and undersampled positive class by factor 0.4 -> better balanced dataģ. I try to build the best decision tree on that data as possible.ġ. I have very unbalanced data ( 3000 positive, 80 negative ) with about 60 predictors (numeric) and one label (binary)ġ. There are a lot of discussions about unbalanced data in this forum, but i cannot crack my problem anyway.ġ.
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