Feature Statistic Document For Free

Orange is the perfect tool for hands-on training. Teachers enjoy the clear program design and the visual explorations of data and models. Students benefit from the flexibility of the tool and the power to invent new combinations of data mining methods.

Features. Orange consists of a canvas interface onto which the user places widgets and creates a data analysis workflow. Widgets offer basic functionalities such as reading the data, showing a data table, selecting features, training predictors, comparing learning algorithms, visualizing data elements, etc.

Features Canvas: graphical front-end for data analysis. Widgets: Data: widgets for data input, data filtering, sampling, imputation, feature manipulation and feature selection. Visualize: widgets for common visualization (box plot, histograms, scatter plot) and multivariate visualization (mosaic display, sieve diagram).

Key features provided by data mining software include data preparation capabilities, built-in algorithms, predictive modeling support, a GUI-based development environment, and tools for deploying models and scoring how they perform.

It is an open-source data visualization, data mining, and machine learning tool. In Orange, it is a scriptable setting for fast prototyping of the current algorithms and testing designs. It is a set of python-based modules that lie in the center library.

The Feature Statistics widget provides a quick way to inspect and find interesting features in a given data set. The Feature Statistics widget on the heart-disease data set. The feature type - can be categorical, numeric, time and string.