Orange includes many standard visualizations. Scatter plot is great for visualizing correlations between pair of attributes, box plot for displaying basic statistics, heat map to provide an overview across entire data set, and projection plots like MDS for plotting the multinomial data in two dimensions.
Orange includes many standard visualizations. Scatter plot is great for visualizing correlations between pair of attributes, box plot for displaying basic statistics, heat map to provide an overview across entire data set, and projection plots like MDS for plotting the multinomial data in two dimensions.
Orange is an open-source data visualization, machine learning and data mining toolkit. It features a visual programming front-end for exploratory qualitative data analysis and interactive data visualization.
Personally, I think some of the benefits of Orange include its rich visualizations, interactive models, and speed. I code mostly in Python but often use Orange to get a quick look at what the data looks like or cross validate a model's performance I am developing elsewhere very quickly.
Top industries that use Orange for data-visualization are Artificial Intelligence (3), Machine Learning (3), Student Services (2).
Orange is a component-based visual programming software package for data visualization, machine learning, data mining, and data analysis.
I'd recommend Orange to discover machine learning & data mining, to see how algorithms behave. You'll have fun with the interface.
Cons: We aren't able to set. seed in the software which causes results and analysis to not be reproducible. Orange does not provide enough parameters to tweak for advanced users. For instance, the randomForest widget does not allow the user to see which variables have the highest information gain.
The hex code for orange is #FFA500.