Machine Learning Overfitting vs Underfitting #shorts #youtubeshort #ml #datascience #aiml

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Shared February 1, 2026

𝗨𝗻𝗱𝗲𝗿𝗳𝗶𝘁𝘁𝗶𝗻𝗴 𝘃𝘀 𝗢𝘃𝗲𝗿𝗳𝗶𝘁𝘁𝗶𝗻𝗴 ⚖️ Machine Learning Overfitting vs Underfitting #shorts #youtubeshort #interviewtips #ml #datascience #ZeroToDataScience #machinelearningmagic #DataScienceInterviewQuestions #interviewtips #PythonForDqtaScience (𝑶𝒏𝒆 𝒐𝒇 𝒕𝒉𝒆 𝑴𝑶𝑺𝑻 𝒂𝒔𝒌𝒆𝒅 𝑴𝑳 𝒒𝒖𝒆𝒔𝒕𝒊𝒐𝒏𝒔) 𝑴𝒂𝒏𝒚 𝑴𝑳 𝒎𝒐𝒅𝒆𝒍𝒔 𝒇𝒂𝒊𝒍 𝒏𝒐𝒕 𝒃𝒆𝒄𝒂𝒖𝒔𝒆 𝒐𝒇 𝒂𝒍𝒈𝒐𝒓𝒊𝒕𝒉𝒎𝒔 B𝒖𝒕 𝒃𝒆𝒄𝒂𝒖𝒔𝒆 𝒕𝒉𝒆𝒔𝒆 𝒃𝒂𝒔𝒊𝒄𝒔 𝒂𝒓𝒆 𝒎𝒊𝒔𝒖𝒏𝒅𝒆𝒓𝒔𝒕𝒐𝒐𝒅. 𝑳𝒆𝒕’𝒔 𝒇𝒊𝒙 𝒕𝒉𝒂𝒕 👇 🧠𝑻𝒓𝒂𝒊𝒏𝒊𝒏𝒈 𝑫𝒂𝒕𝒂 𝒗𝒔 𝑻𝒆𝒔𝒕𝒊𝒏𝒈 𝑫𝒂𝒕𝒂 1. Training Data Used to teach the model The model learns patterns from this data 📌 Think: Study material 2. Testing Data Used to evaluate the model Never shown during training 📌 Think: Final exam ⚠️ 𝑼𝒏𝒅𝒆𝒓𝒇𝒊𝒕𝒕𝒊𝒏𝒈 𝒗𝒔 𝑶𝒗𝒆𝒓𝒇𝒊𝒕𝒕𝒊𝒏𝒈 1. 𝑼𝒏𝒅𝒆𝒓𝒇𝒊𝒕𝒕𝒊𝒏𝒈 Model is too simple Fails on training data ❌ Fails on testing data ❌ 🧠 Meaning: Model didn’t learn enough Example: Linear model for complex data 2. 𝑶𝒗𝒆𝒓𝒇𝒊𝒕𝒕𝒊𝒏𝒈 The Model is too complex Performs very well on training ✅ Performs poorly on testing ❌ 🧠 Meaning: Model memorized, not learned Example: Very deep model on small data ⚖️𝗧𝗵𝗲 𝗚𝗼𝗮𝗹: 𝗚𝗼𝗼𝗱 𝗙𝗶𝘁 1. Performs well on training ✅ 2. Performs well on testing ✅ 📌 This is where real ML works 🎯 One-Line Interview Answer Underfitting means the model is too simple to learn patterns, While overfitting means the model learns noise instead of general patterns. 💡 𝑭𝒊𝒏𝒂𝒍 𝑻𝒉𝒐𝒖𝒈