{"id":3777,"date":"2026-04-02T16:28:16","date_gmt":"2026-04-02T14:28:16","guid":{"rendered":"https:\/\/thedatastory.nl\/?p=3777"},"modified":"2026-04-02T16:31:59","modified_gmt":"2026-04-02T14:31:59","slug":"beyond-accuracy-how-to-evaluate-unsupervised-models-for-reliable-data-insights","status":"publish","type":"post","link":"https:\/\/thedatastory.nl\/en\/data-stories\/beyond-accuracy-how-to-evaluate-unsupervised-models-for-reliable-data-insights\/","title":{"rendered":"Beyond Accuracy: How to Evaluate Unsupervised Models\u00a0for Reliable\u00a0Data\u00a0Insights\u00a0\u00a0"},"content":{"rendered":"
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Unsupervised learning is a form of machine learning that\u00a0identifies\u00a0patterns and structures in data without relying on labelled examples or predefined outcomes. That is both its greatest strength and its biggest challenge. It can uncover valuable insights even when we do not yet know what we are looking for.\u00a0Without labels\u00a0however, how do you know whether the model is performing well? Traditional metrics such as accuracy cannot\u00a0provide\u00a0the answer.\u00a0<\/p>\n\n

That does not mean model quality cannot be evaluated. It simply means we need to approach the problem differently. In this blog, we explain how we tackled that challenge in a project for one of the largest banks in the Netherlands.<\/p>\n\n

How can you evaluate unsupervised models in practice?<\/strong>\u00a0<\/h2>\n\n

In this project, our goal was to improve transaction monitoring using an isolation forest: an unsupervised learning algorithm designed to detect anomalies. In this context, those anomalies may point to potentially fraudulent transactions.\u00a0<\/p>\n\n

The challenge was clear. As it was unknown in advance which transactions were genuinely fraudulent and which were not, we could not evaluate the model in the traditional sense. We had no direct way to measure whether the model was separating suspicious transactions from legitimate ones correctly.\u00a0<\/p>\n\n

To address this, we shifted our focus from\u00a0accuracy<\/strong>\u00a0to\u00a0consistency<\/strong>.\u00a0\u00a0<\/p>\n\n

Our reasoning was straightforward: if the same transaction is repeatedly flagged as anomalous across different versions of the data, it is more likely to be a genuinely unusual case. In other words, if the model consistently\u00a0identifies\u00a0the same transactions as anomalous across multiple datasets, this increases our confidence that it is producing meaningful results.\u00a0<\/p>\n\n

To\u00a0put this into practice, we built an evaluation framework based on cross-validation. Across many iterations, the data was split into training and test sets. In each iteration, the isolation forest was trained on the training set and then used to evaluate the test set.\u00a0<\/p>\n\n

For every transaction, we\u00a0recorded:\u00a0<\/p>\n\n

    \n
  • how often it appeared in the test set,\u00a0<\/li>\n<\/ul>\n\n
      \n
    • how often it was classified as anomalous,\u00a0<\/li>\n<\/ul>\n\n
        \n
      • and how often it was classified as non-anomalous.\u00a0<\/li>\n<\/ul>\n\n


        This gave us a way to assess how consistently each transaction was categorized.\u00a0<\/p>\n\n

        A transaction with\u00a0low consistency<\/strong>\u00a0would be classified as anomalous about as often as it was classified as non-anomalous. A transaction with\u00a0high consistency<\/strong>\u00a0would be assigned to the same category\u00a0almost every\u00a0time it appeared in the test data.\u00a0<\/p>\n\n

        We quantified this by taking, for each transaction, the higher of the two classification counts and dividing it by the number of times that transaction appeared in the test set. This produces a consistency score between\u00a00.5 and 1.0<\/strong>, where a higher score\u00a0indicates\u00a0more stable classification.\u00a0<\/p>\n\n

        By averaging these scores across all transactions, we obtain a global consistency score. This\u00a0provides\u00a0a practical way to compare different isolation forest implementations and\u00a0determine\u00a0which one produces more reliable results.\u00a0<\/p>\n\n

        Don\u2019t\u00a0let complexity hold you back<\/strong>\u00a0<\/h2>\n\n

        This project shows that valuable solutions in data science do not always come from standard methods or off-the-shelf metrics. In this case, we developed a practical way to compare models when conventional evaluation was not possible, allowing us to provide the most reliable model for the client with confidence.\u00a0<\/p>\n\n

        More broadly, data science is full of situations where the\u00a0real challenge\u00a0lies not just in building models, but in framing problems correctly and designing approaches that work in practice. That is where data science delivers its real value: creativity, critical thinking, and domain\u00a0expertise\u00a0make it possible to develop solutions that truly fit the context at hand.\u00a0<\/p>\n\n

        Final words<\/strong>\u00a0<\/h2>\n\n

        Are you looking for new ways to get more out of your data?\u00a0Don\u2019t\u00a0hesitate to reach out to our team of experts.\u00a0<\/p>\n\n

        \u00a0<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>","protected":false},"excerpt":{"rendered":"

        Unsupervised learning is a form of machine learning that\u00a0identifies\u00a0patterns and structures in data without relying on labelled examples or predefined outcomes. That is both its greatest strength and its biggest […]<\/p>\n","protected":false},"author":2,"featured_media":3779,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_price":"","_stock":"","_tribe_ticket_header":"","_tribe_default_ticket_provider":"","_tribe_ticket_capacity":"0","_ticket_start_date":"","_ticket_end_date":"","_tribe_ticket_show_description":"","_tribe_ticket_show_not_going":false,"_tribe_ticket_use_global_stock":"","_tribe_ticket_global_stock_level":"","_global_stock_mode":"","_global_stock_cap":"","_tribe_rsvp_for_event":"","_tribe_ticket_going_count":"","_tribe_ticket_not_going_count":"","_tribe_tickets_list":"[]","_tribe_ticket_has_attendee_info_fields":false,"footnotes":""},"categories":[2],"tags":[],"class_list":["post-3777","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-stories"],"yoast_head":"\nBeyond Accuracy: How to Evaluate Unsupervised Models\u00a0for 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