Most multi-class classifiers make their prediction for a test sample by
scoring the classes and selecting the one with the highest score. Analyzing
these prediction scores is useful to understand the classifier behavior and to
assess its reliability. We present an interactive visualization that
facilitates per-class analysis of these scores. Our system, called Classilist,
enables relating these scores to the classification correctness and to the
underlying samples and their features. We illustrate how such analysis reveals
varying behavior of different classifiers. Classilist is available for use
online, along with source code, video tutorials, and plugins for R, RapidMiner,
and KNIME at this https URL

通过评分各个类别并选择得分最高的类别进行测试样本的分类，以此来分析分类器的行为并评估其可靠性。本文介绍了一种交互式可视化工具 ——Classilist，可方便地针对每个类别分析这些预测分数，将这些分数与分类正确性及底层样本和其特征相关联，展示不同分类器的不同行为。同时，Classilist 可通过该 https URL 在线使用，提供源代码、视频教程以及 R、RapidMiner 和 KNIME 的插件。