{
  "id": 13635423,
  "title": "Three Things I Found When I Revisited My Gender Classification Project",
  "url": "https://urgent.news/2026/10/11/three-things-i-found-when-i-revisited-my-gender-classification-project",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-11T04:00:00.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/ayush_pangaonkar/three-things-i-found-when-i-revisited-my-gender-classification-project-13mb"
  },
  "original_language": "en",
  "account": "In revisiting the Gender Classification project, three significant findings emerged. First, approximately one-third of the original dataset consisted of duplicate rows—1,768 out of 5,001 rows, accounting for about 35% of the data. After removing these duplicates, the dataset reduced to 3,233 entries. Additionally, the class balance shifted following the removal of duplicates, moving from an almost even distribution of 2,500 males to 2,501 females to a new ratio of 1,783 males (55.1%) and 1,450 females (44.9%).\n\nSecond, the simplest model, Logistic Regression, proved to be the best performer in terms of generalizing to unseen data. The model achieved a training accuracy of 95.1% and a test accuracy of 96.0%. In contrast, both the K-Nearest Neighbors (KNN) model with k=3 and the Decision Tree, while showing higher training accuracies (97.0% and 99.8% respectively), demonstrated poorer performance on the test set, with test accuracies of 94.4% each. Notably, the Decision Tree exhibited a significant gap between its training and test accuracies, indicating potential overfitting, a pattern observed in the author's previous admissions project.\n\nLastly, the feature that the author initially anticipated to have the most significant impact on the model's performance turned out to be less influential. The coefficients of the scaled features, as determined by Logistic Regression, revealed that facial measurements—specifically, the variables nose_wide, lips_thin, and distance_nose_to_lip_long—carried the most weight, with coefficients of 3.60, 3.34, and 3.28 respectively. Conversely, the length of hair, which the author had expected to be the most significant predictor, had a relatively minor coefficient of 0.07. This finding suggests that while hair length might appear intuitive, it is not the primary factor influencing the gender classification in this dataset. The facial dimensions, however, emerge as the dominant predictors.",
  "summary": "I went back to my Gender Classification project, a learning exercise on a small tabular dataset, and found three things worth writing down. (It is a classroom-style dataset of facial and physical measurements, not something I would build a real product on.) The setup The dataset, gender_classification.csv , has 5,001 rows. The features are long_hair , forehead_width_cm , forehead_height_cm ,…",
  "key_points": [],
  "editors_take": "The reevaluation of the Gender Classification project reveals that removing dataset duplicates significantly alters class balance and that facial measurements, not hair length, are the strongest predictors of gender classification.",
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}