@user3901868842355: 🤗🤗🤗🤗

🎀🍫মায়াবতী🍫🎀

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Wednesday 10 June 2026 10:39:59 GMT

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মায়াবতী 🥰🥰 :

ক্রাশ খাইছি গো

2026-06-13 09:47:11

4

কুমিল্লার পোলা ✌️✌️✌️ :

আই লাভ ইউ😁😁🫣🫣🫣

2026-06-10 11:34:30

8

Md Nazmul🥰🥰🥰🥀🌾🕊️ :

তাই নাকি 😳

2026-06-10 16:56:36

5

MD Hafiz Kari jubayer haolader :

সাপোর্ট চাই

2026-06-10 15:20:52

3

আলোর পথ... 🕊️🕊️🫀 :

"সব কথা তো মুখে বলা যায় না, কিছু কথা চোখেই জমা থাকে। আর সেই না-বলা কথাগুলো বুঝে নেওয়ার জন্য শুধু একজন বিশ্বস্ত মানুষের প্রয়োজন হয়।"🌸

2026-06-13 06:34:30

2

💠🔹مُحَمَّدُ أَبُو طَلْحَ🔹💠 :

ইতনা সুকুন 🫠

2026-06-15 01:30:20

2

𝓜𝓓.𝓱𝓪𝓲𝓻𝓾𝓵𝓵 𝓮𝓼𝓵𝓲𝓶 :

চক গুলো কিন্তু অনেক সুন্দর

2026-06-10 12:11:26

2

😘পিচ্চি হুজুর💝 :

এই যুগের ফেতনা 🥺😔

2026-06-11 03:36:29

5

❇️🕋❣️আমি তো সেই জাহিদ 🕋❇️ :

সাপোর্ট করবেন আপনি আপু

2026-06-12 05:08:38

0

দুষ্টু রাজকন্যা :

ওহ আচ্ছা দিও ভালোবাসা তাকে

2026-06-11 12:48:36

0

💞★"মায়াবী হুজুর"★💞 :

আসসালামু আলাইকুম

2026-06-13 03:20:56

2

Mr. Saidul Hassan Hassan :

মাশাআল্লাহ

2026-06-10 17:16:55

1

sigma boy... :

মাশাল্লাহ 🥰

2026-06-13 00:53:32

1

💫💫সুখ.তারা💫💫 :

সুন্দর ×অপরূপ সুন্দর 👈🥰

2026-06-13 16:35:05

2

md.ashik :

সেলিব্রেটি হয়ে কমেন্ট করলাম

2026-06-10 18:13:12

2

✿☻—͞𝐘𝐨𝐔𝐫 ♡𝐕𝐚𝐢𝐘𝐚⎯⃝☻🎀☻ :

খুব সুন্দর লাগছে

2026-06-10 11:37:14

1

আমি তোমার প্রেমে আসক্ত।৪৫৪৫ :

আপু গো ফলো বেক দাওনা

2026-06-11 23:31:23

1

🛺অটোপার্স সিনিয়র ইন্জিনিয়ার :

চোখ দুটো পুরোই মায়াতে ভরা । impressed with you

2026-06-10 17:38:24

1

MD Maruf Hossain :

আপনার চোখ দেখে আমি তো ক্রাস খাইছি

2026-06-15 11:22:29

1

🥀🥀,মনে, হয় ভালোবাসা,.🥀🥀 :

এতো কষ্ট কিসের 🥺🥺

2026-06-13 01:22:21

1

meheraj :

nice🥰🥰🥰

2026-06-10 17:24:10

1

🌿ক্ষুৎিপপাসু তক্ষশীলা🌿 :

ব

2026-06-10 17:11:59

1

S I A M :

2026-06-11 03:43:09

1

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Decision Trees are the foundation of many advanced machine learning models like XGBoost and Random Forest – but their true power lies in how simple and intuitive they are. Here is how it works: 1️⃣ Starting point: The tree begins with all your data and looks for the one feature that best separates the group (e.g., “Is the transaction over $500?”) 2️⃣ Splitting the data: Based on that answer, the data is divided into two separate paths. 3️⃣ Continue splitting: The model repeats this process for each path, asking deeper and more specific questions to group similar items together. 4️⃣ Final decision: Once the model runs out of questions or hits a set limit, it lands on a final answer to categorize the data point. A few key things to know: ✅ Human-Readable: You can literally trace the path from start to finish to see exactly why the model made its decision. ✅ Flexible: They handle both numbers and categories easily without needing complex math transformations. ✅ Smart Splitting: The algorithm chooses its “questions” by looking for the biggest jump in clarity—essentially trying to keep similar things together as it drills down. The Trade-offs: ⚠️ The Over-Thinking Trap: Without limits, a tree can keep asking questions until it memorizes your training data perfectly, which makes it perform poorly on new, real-world data (this is called overfitting). ⚠️ Fragile Logic: A tiny change in your data can sometimes trigger a completely different series of questions, changing the whole tree. ⚠️ Greedy: The model only looks for the best immediate split, not necessarily the best structure for the entire tree long-term. Despite these quirks, they remain the essential “DNA” of modern machine learning. #DataScience #MachineLearning #DecisionTrees #AI #Algorithms

Decision Trees are the foundation of many advanced machine learning models like XGBoost and Random Forest – but their true power lies in how simple and intuitive they are. Here is how it works: 1️⃣ Starting point: The tree begins with all your data and looks for the one feature that best separates the group (e.g., “Is the transaction over $500?”) 2️⃣ Splitting the data: Based on that answer, the data is divided into two separate paths. 3️⃣ Continue splitting: The model repeats this process for each path, asking deeper and more specific questions to group similar items together. 4️⃣ Final decision: Once the model runs out of questions or hits a set limit, it lands on a final answer to categorize the data point. A few key things to know: ✅ Human-Readable: You can literally trace the path from start to finish to see exactly why the model made its decision. ✅ Flexible: They handle both numbers and categories easily without needing complex math transformations. ✅ Smart Splitting: The algorithm chooses its “questions” by looking for the biggest jump in clarity—essentially trying to keep similar things together as it drills down. The Trade-offs: ⚠️ The Over-Thinking Trap: Without limits, a tree can keep asking questions until it memorizes your training data perfectly, which makes it perform poorly on new, real-world data (this is called overfitting). ⚠️ Fragile Logic: A tiny change in your data can sometimes trigger a completely different series of questions, changing the whole tree. ⚠️ Greedy: The model only looks for the best immediate split, not necessarily the best structure for the entire tree long-term. Despite these quirks, they remain the essential “DNA” of modern machine learning. #DataScience #MachineLearning #DecisionTrees #AI #Algorithms

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