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ข้าวฟ่าง อาทิตยา🦋🌻
ข้าวฟ่าง อาทิตยา🦋🌻
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Saturday 19 July 2025 08:41:08 GMT
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Outliers = extreme values that don’t “fit” with the rest of the data ⚠️ They can distort averages, scaling, and ML models if not handled properly. One simple way to detect them is using Percentiles. 🔹 How Percentile Method Works? 1️⃣ Choose lower & upper thresholds (commonly 1st and 99th, or 5th and 95th percentiles). 2️⃣ Any value below the lower bound or above the upper bound is considered an outlier. 📌 Example: If Salary < 1st percentile OR > 99th percentile → mark as outlier. ✅ Advantages: • Very simple to implement • Doesn’t assume data distribution (non-parametric) • Works well for skewed data ❌ Disadvantages: • Arbitrary cutoff points • May remove valid extreme values • Not suitable for very small datasets 💡 Key Takeaway: Percentile method = quick & easy way to detect outliers. Best used when you want a fast, distribution-free check 🚀 #️</h>⃣ <h id=#OutlierDetection #PercentileMethod #DataPreprocessing #MachineLearning #DataScience #100DaysOfML" width="135" height="240">
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Đau lòng nhất của một đứa trẻ là chính mẹ của mình không còn nhận ra - Gia đình em Trần Hoàng Võ - Mái ấm gia đình Việt tập 206 #maiamgiadinhviet #hoasenhome #tonhoasen #vhsh #hsh
Đau lòng nhất của một đứa trẻ là chính mẹ của mình không còn nhận ra - Gia đình em Trần Hoàng Võ - Mái ấm gia đình Việt tập 206 #maiamgiadinhviet #hoasenhome #tonhoasen #vhsh #hsh

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