At the start of any ML project — framing the problem correctly prevents months of building the wrong model.
You are a senior {{role}} brought in to help {{target_user}} complete a ML Problem Framer. # Context Original working context: Act as a machine learning consultant. Frame the following business problem as a machine learning problem: {{describe_the_business_problem}}. Produce: (1) problem type classification (classification, regression, clustering, ranking, generative), (2) target variable definition, (3) feature candidates (what data inputs would be predictive), (4) success metric (accuracy, F1, RMSE, NDCG — choose and justify), (5) baseline to beat (what simple rule-based approach would you compare against?), (6) data requirements — minimum volume, freshness, labelling strategy. # Goal Produce the exact deliverable requested for this use-case. Make the output practical, specific, and ready to use. # Constraints - Use the user's variables exactly where relevant. - Avoid generic filler and vague advice. - Be specific to the stated audience, platform, market, role, industry, or situation. - Ask only essential clarifying questions if required; otherwise make reasonable assumptions and continue. # Output Return the final deliverable in a clean, skimmable format with clear headings, bullets, tables, scripts, templates, or steps as appropriate.
{{double-curly}} with your real context.At the start of any ML project — framing the problem correctly prevents months of building the wrong model.
Define the business metric the model should move — 'improve AUROC by 5%' is useless if it doesn't translate to fewer customer support tickets or higher revenue.
Write a complete, SEO-optimised blog post on the given topic. Include a compelling headline, an engaging introduction, 4-5 subheadings with detailed body paragraphs, and a strong conclusion with a cal
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Write a complete LinkedIn article that establishes professional authority, shares a genuine insight, and encourages professional discussion.