{"slug": "2nd-degree-ml-thoughts", "title": "2nd degree ML Thoughts", "summary": "A blog post explains machine learning concepts, stating that feature scaling works in supervised learning because models learn patterns of X to Y, so normalizing input data (e.g., X/2) maps to normalized output (Y/2). It also describes logistic regression as wrapping linear regression in a sigmoid function to output probabilities between 0 and 1, with the model learning to minimize loss for classification.", "body_md": "# 2nd degree ML Thoughts\n\n## Feature Scaling.\n\nAs far as supervised learning, feature scaling works, it might be counter intuitive to reduce the training data set, to some none meaning number, because in ML learning, it was never to learn about input data, it was learning patterns of X → Y. so that next time X some it close to Y.\n\nif we teach model X → Y, or X^2 → Y^2 or X/2 → Y/2, next time for input X’, if we do the `normilization`\n\n. then say X’ = X’/2, then it will be mapped after learning to Y/2.\n\n## How Logistics Regression works by..\n\nby just wrapping a Linear Regression in `sigmoid`\n\n. ?\n\nSince `sigmoid`\n\nis a cleaver function who’s property is to output just `0 -> 1`\n\nanswer, we create our model, wrapping Linear Regression in `simod`\n\nwhich become’s our `MODEL`\n\n. so that model **Learns** to `minimize the loss`\n\nbetween this outputted (y’hat) and the classification, which is actually just 0 and 1. so our MODEL learns (i.e set optimal paramters) which are optimized to give near correct answer tof rnext input in classification.\n\nFor next input, (test time). we still get a probability between 0 → 1 for the Probability of Y, i.e if y == 1 then probability of 1 else 0.\n\n[← Back to All Blogs](/blog)", "url": "https://wpnews.pro/news/2nd-degree-ml-thoughts", "canonical_source": "https://knl.co.in/blog/2nd-degree-ml/", "published_at": "2026-08-23 15:34:00+00:00", "updated_at": "2026-08-24 17:45:59.323601+00:00", "lang": "en", "topics": ["machine-learning"], "entities": [], "alternates": {"html": "https://wpnews.pro/news/2nd-degree-ml-thoughts", "markdown": "https://wpnews.pro/news/2nd-degree-ml-thoughts.md", "text": "https://wpnews.pro/news/2nd-degree-ml-thoughts.txt", "jsonld": "https://wpnews.pro/news/2nd-degree-ml-thoughts.jsonld"}}