Training Data Size Calculator

Model Complexity sets a per-feature-per-class sample multiplier that reflects how much a model class can overfit: Linear/Logistic models have relatively few parameters and a well-understood, low-variance fit, needing roughly 20 samples per feature; Deep Neural Networks have vastly more parameters and can memorize small datasets instead of generalizing, so the rule-of-thumb multiplier jumps to roughly 2,000 samples per feature to keep the model learning real patterns rather than noise.

Below a 70% target this calculator's accuracy multiplier sits at its floor value -- the same value used at exactly 70% -- rather than continuing to shrink, since the base per-feature sample count set by Model Complexity is treated as the minimum viable dataset regardless of how modest the accuracy goal is. The multiplier only grows once Target Accuracy (%) climbs ABOVE 70%, reflecting that each additional accuracy point above a modest baseline costs disproportionately more data than the last.

The accuracy multiplier scales with the SQUARE of how far Target Accuracy (%) sits above 70%, so the gap between 80% and 90% (10 points) adds far less than the gap between 90% and 99% (9 points) does -- squaring means the marginal data cost accelerates as the target approaches the ceiling, mirroring the real-world "diminishing returns" pattern where the last few accuracy points are the most expensive to earn.

It multiplies the total: Samples per Class is computed first from Number of Features, Model Complexity, and Target Accuracy (%), then Minimum Total Samples is that per-class figure times Number of Classes. A 10-class problem is therefore estimated to need roughly 5x the total data of a comparable 2-class problem, not the same total split five ways thinner, since each class still needs enough examples on its own.

No -- it applies a single flat assumption of about 2 minutes of manual effort per sample to Minimum Total Samples, regardless of whether labeling means a simple category click or a complex multi-step annotation. Treat it as a rough labor-planning placeholder; a genuinely difficult labeling task (medical imaging, detailed bounding boxes) will take far longer per sample than this estimate assumes.