#Overfitting Vs Underfitting Graph

99% accuracy? Your model might be cheating. 🎯 Overfitting vs Underfitting — the most important concept in machine learning. Too simple → misses the pattern (underfitting) Too complex → memorizes noise (overfitting) Just right → actually learns (generalization) The goal isn't to fit training data perfectly. It's to perform well on data the model has never seen. That's the bias-variance tradeoff. — Follow @dailymathvisuals for more math visuals. #overfitting #underfitting #machinelearning #datascience #ai #deeplearning #biasvariance #modeltraining #python #coding #tech #stemcreator #learnai #artificialintelligence

Too simple… the model learns nothing. Too complex… the model memorizes everything. That’s the balance between Underfitting and Overfitting. In machine learning, the goal isn’t just to learn the data — it’s to learn the pattern. If you’re learning ML, understanding this balance is key. #machinelearning #datascience #artificialintelligence #overfitting #underfitting mlconcepts aieducation aireels techreels datasciencereels learnml buildinpublic techcreators womenintech futureofai viralreels explorepage

Overfitting occurs when a model memorizes the training data too precisely, including its noise and randomness, instead of learning the general underlying pattern. Imagine fitting curves to data points that roughly follow a parabolic shape. A linear model is too basic. Since it can’t capture the curve, it underfits, resulting in high error on both training and test data. A quadratic model reflects the true structure of the data, producing low training and test error - this is the right balance. But if you move to a cubic model, it may warp itself to pass exactly through every training point. This gives extremely low training error, but it fails to generalize. On new data, the predictions swing too much, causing high test error. That behavior is overfitting: great performance on data the model has seen, poor performance on data it hasn’t. C: Welch Labs #machinelearning #deeplearning #datascience #python #programming #computerscience #tech #coding #pythonprogramming #datascientist

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To make the mathematical concept of overfitting visually accessible, I simplified certain technical details in this video. First, a linear fit in three or more dimensions is not technically a "straight line," but rather a flat plane or a hyperplane. Second, we do not convert the data points themselves into individual dimensions. Instead, we generate new polynomial features (such as x^2, x^3, and so on). However, when the number of these higher-degree features approaches the total number of data points, the model gains enough degrees of freedom to memorize the training data rather than learning the underlying pattern. Ultimately, the complex curve you see in a two-dimensional projection is mathematically a flat, rigid hyperplane in a higher-dimensional space.

Let’s do the math. 👇 Step 1. Estimate your body fat percentage. Round up to be safe. I did my best with the AI relax… Step 2. Find your fat free mass. Current body weight x 0.75 = lean mass. At 250 pounds that’s 187.5 pounds of lean mass. Step 3. Find your target weight. Fat free mass divided by 0.9 = your weight at 10% body fat. That gives us 208.3 pounds. No this isn’t perfect but it gives us a good starting point… Step 4. Find out how much you need to lose. 250 minus 208.3 = 41.7 pounds to lose. Step 5. Figure out your timeline. At 2 pounds per week that’s 21 weeks. At 1 pound per week that’s 42 weeks, but you’ll definitely have to adjust along the way… Losing more than 2 pounds per week isn’t healthy and it isn’t sustainable. Now be smart. And make sure you follow @macro.bart #fatloss #bodyfat #fatlosstimeline #fatlosseducation #fatlossmath

Harper Carroll on fixing underfitting in your model:⁣ ⁣ "Some ways to mitigate that are to increase the model complexity. So add layers, add neurons."⁣ ⁣ "You could also try reducing regularization. So if you have dropout, L1 or L2 regularization, then just remove that."⁣ ⁣ "You could also try just training for more epochs... they found that increasing the number of training epochs drastically increased the model's performance on languages with less data."

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You might be shorter than you actually are… Yes — most people measure their height WRONG. And the truth? You can lose up to 2 cm just by measuring at the wrong time. Here’s the correct way: ✔ Back straight against the wall ✔ Flat object on your head ✔ Heels touching the wall ✔ Measure in the morning (this is key) Your real height is higher than you think. Don’t guess. Measure it right. Discover how to unlock your full height potential 👉 www.growtallerways.com #growtaller #heightgrowth #heightincrease #tallertips #heightcheck measurecorrectly biohackingbody posturefix growtallernaturally selfimprovement healthoptimization morninghack heightsecrets tiktokfitness viralhealth growtallerways

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Can you draw a curve instead of a line of best fit in your Chemistry ATP exam?

Did you know this about grading inseams?? Because apparently I had an inkling deep in my brain, but didn’t know why or how to execute it properly😅 I know length is probably one of the most common adjustments people would make when sewing their own clothes. So it’s not imperative. I just think these little nuances in grading are super interesting. And I love learning ones that hadn’t quite stuck for me before. #patternmaking #patterncutting #patterndesign #sewistofinstagram #sewistsofinstagram @itsclo3d #clo3d #clocreator #patterngrading