Screenshots

Paint a two-dimensional data set

Data selection in scatter plot is visualised in a box plot

Heatmap visualisation

Sieve diagram on a titanic data set

Box plot and class-based grouping

Explorative analysis and classification trees

Data can contain references to images

Principal component analysis

Hierarchical clustering

Playing with a paint data and automatic selection of clusters in k-means

Feature scoring and ranking

Model-based feature scoring

Polynomial regression

Predictions of a linear regression model on a test set

Data preprocessing turned into component of a learning algorithm

Cross-validation and scoring of classifiers

Visualizing misclassifications

Receiver operating characteristics (ROC) analysis

Cross-validated calibration plot

Venn diagram identifies a missclassification

Orange can guess which widget to add to a developing schema