Screenshots

  • Load and edit your data in the File widget.

    Load and edit your data in the File widget.

  • Paint a two-dimensional data set.

    Paint a two-dimensional data set.

  • Data selection in Scatter Plot is visualised in a Box Plot.

    Data selection in Scatter Plot is visualised in a Box Plot.

  • Orange can suggest which widget to add to the workflow.

    Orange can suggest which widget to add to the workflow.

  • Join two data sets.

    Join two data sets.

  • Box plot displays basic statistics of attributes.

    Box plot displays basic statistics of attributes.

  • Sieve diagram on Titanic data set.

    Sieve diagram on Titanic data set.

  • Heatmap visualisation.

    Heatmap visualisation.

  • Explorative analysis with classification trees.

    Explorative analysis with classification trees.

  • Data can contain references to images.

    Data can contain references to images.

  • Hierarchial clustering supports interactive cluster selection.

    Hierarchial clustering supports interactive cluster selection.

  • Playing with Paint Data and an automatic selection of clusters in k-Means.

    Playing with Paint Data and an automatic selection of clusters in k-Means.

  • Multidimensional scaling of Zoo data set reveals phylogeny groups.

    Multidimensional scaling of Zoo data set reveals phylogeny groups.

  • Principal component analysis with scree diagram.

    Principal component analysis with scree diagram.

  • Receiver operating characteristics (ROC) analysis.

    Receiver operating characteristics (ROC) analysis.

  • Cross-validated calibration plot.

    Cross-validated calibration plot.

  • Data preprocessing embedded within a learning algorithm.

    Data preprocessing embedded within a learning algorithm.

  • Feature scoring for finding interesting data projections.

    Feature scoring for finding interesting data projections.

  • Model-based feature scoring.

    Model-based feature scoring.

  • Cross-validated calibration plot.

    Cross-validated calibration plot.

  • Visualizing misclassifications.

    Visualizing misclassifications.

  • Finding common misclassifications of three predictive models.

    Finding common misclassifications of three predictive models.

  • Model testing and scoring on a separate test data set.

    Model testing and scoring on a separate test data set.

  • Intersection of misclassified data and data with low silhouette score.

    Intersection of misclassified data and data with low silhouette score.

  • CN2 rule induction.

    CN2 rule induction.

  • Showcase for approximation by regression tree.

    Showcase for approximation by regression tree.

  • Interactive gradient descent.

    Interactive gradient descent.

  • Predicting text categories.

    Predicting text categories.

  • Topic modelling of recent tweets.

    Topic modelling of recent tweets.

  • Image analytics with deep-network embedding.

    Image analytics with deep-network embedding.

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