Search results
As you mentioned, Orange is a data mining software developed by the University of Ljubljana. It can be used for developing and testing machine learning models as well as conducting exploratory data analysis and visualization. One of the unique features that makes Orange "special" is its simplicity and ease of use.
Jun 27, 2018 · 2. I need to split my datasets based on my own feature column in order to hold together certain data rows (e.g. from one patient, or compound) for cross-validation (CV). In Orange 3.11, Test&Score contains "Cross validation by feature", but it is disabled. The widget documentation contains a screenshot still without it.
Sep 13, 2018 · In Orange, the term domain denotes a set of variables and meta attributes that describe data. A domain descriptor is attached to data instances, data tables, classifiers and other objects. A descriptor is constructed, for instance, after reading data from a file.
Dec 14, 2020 · I am using the Orange data mining tool to build and analyze models (decision tree, ANN, ...) predicting customer churn. As this is an imbalanced class problem (10% churn, 90% not churn), I need to oversample within the cross validation. However, I am not totally able to implement this by myself.
Oct 11, 2022 · Orange Data Mining Tool on Google Colab. Ask Question. Asked 1 year, 11 months ago. Modified 1 year, 11 months ago. Viewed 293 times. 1.
I am planning to use the Orange Data Mining Tool for easy data exploration and model generation. What is still unclear to me is: after finding a good model, what can I do with it, how can I use or deploy it in production? I already found out that there is no Orange server which can run the Orange workflow.
May 17, 2020 · I am experimenting with Orange data mining tool. When I use the 'Corpus' widget from the text mining section it gives me the error: Corpus widget error: I have tried many things, but am still unable to resolve this issue. Besides that, in text mining, it does not show import document option in orange v 3.25.
You can try to randomly delete samples from the majority class until there's a 50-50 split in the data. Then you can proceed to split 75% - 25% for training and testing. You can also try to generate more Yes samples via imputation or whatever means may be relevant to the given dataset. Sometimes you have to make the most out of the data you have.
Dec 13, 2020 · Thanks for contributing an answer to Data Science Stack Exchange! Please be sure to answer the question. Provide details and share your research! But avoid … Asking for help, clarification, or responding to other answers. Making statements based on opinion; back them up with references or personal experience. Use MathJax to format equations.
Dec 21, 2017 · Thanks for contributing an answer to Data Science Stack Exchange! Please be sure to answer the question. Provide details and share your research! But avoid … Asking for help, clarification, or responding to other answers. Making statements based on opinion; back them up with references or personal experience. Use MathJax to format equations.
