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Data Science & Machine Learning (@datasciencefun) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 75 684 obunachidan iborat bo'lib, Taʼlim toifasida 2 114-o'rinni va Hindiston mintaqasida 4 348-o'rinni egallagan.

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невідомо sanasidan buyon loyiha tez o‘sib, 75 684 obunachiga ega bo‘ldi.

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“Join this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free For collaborations: @love_data”

Yuqori yangilanish chastotasi (oxirgi ma’lumot 13 Iyun, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Taʼlim toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.

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75 717

What is a major advantage of Random Forest over Decision Trees?

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75 717

Which module is used for Random Forest in scikit-learn?

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75 717

How does Random Forest make the final prediction in classification?

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75 717

What is Random Forest mainly made of?

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75 717

✅ Random Forest Basics🌲🤖 👉 Random Forest is one of the most popular and powerful Machine Learning algorithms. It combines multiple Decision Trees to make better predictions. 🔹 1. What is Random Forest? Random Forest = Collection of many Decision Trees 👉 Instead of relying on one tree, it takes predictions from many trees and gives the final result. This improves: ✔ Accuracy ✔ Stability ✔ Performance 🔥 2. How Random Forest Works Step-by-step: 1️⃣ Create multiple Decision Trees 2️⃣ Train each tree on random data samples 3️⃣ Each tree gives prediction 4️⃣ Final prediction = Majority vote (classification) 🔹 3. Example 👉 Predict if a customer will buy a product. Tree 1 → Yes Tree 2 → Yes Tree 3 → No ✅ Final Prediction → Yes 🔹 4. Implementation (Python)

from sklearn.ensemble import RandomForestClassifier

# Sample data
X = [,,, ]
y = [1, 2, 3, 4, 0]

model = RandomForestClassifier()
model.fit(X, y)

print(model.predict([])[3])

🔹 5. Advantages ⭐ ✔ High accuracy ✔ Reduces overfitting ✔ Handles large datasets well ✔ Works for classification regression 🔹 6. Disadvantages ❌ Slower than Decision Trees ❌ Harder to interpret 🔹 7. Why Random Forest is Important? ✔ Used in real-world applications ✔ Powerful baseline ML model ✔ Frequently asked in interviews 🎯 Today’s Goal ✔ Understand ensemble learning ✔ Learn majority voting ✔ Implement Random Forest model 💬 Tap ❤️ for more!

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75 717

What type of problems can Decision Trees solve?

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75 717

Which of the following is a disadvantage of Decision Trees?

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75 717

Which library module is commonly used for Decision Trees in Python?

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75 717

What is the starting node of a Decision Tree called?

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75 717

What does a Decision Tree mainly use to make predictions?

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𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 & 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗙𝗥𝗘𝗘 𝗠𝗮𝘀𝘁𝗲𝗿𝗰𝗹𝗮𝘀𝘀😍 Kickstart Your Data Science Career In Top Tech Companies 💫Learn Tools, Skills & Mindset to Land your first Job 💫Join this free Masterclass for an expert-led session on Data Science Eligibility :- Students ,Freshers & Working Professionals 𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘 :- https://pdlink.in/42hIcpO ( Limited Slots ..Hurry Up‍ ) 🔥Date & Time :- 8th May 2026 , 7:00 PM

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✅ Decision Trees Basics🌳🤖 👉 Decision Trees are one of the most intuitive ML algorithms — they work like a flowchart. 🔹 1. What is a Decision Tree? A Decision Tree is a model that makes decisions by splitting data into branches. 👉 It asks questions like: - Is age > 18? - Is salary > 50k? Based on answers → it predicts output. 🔥 2. Structure of a Decision Tree 🌳 Root Node → Starting point 🌿 Branches → Conditions (Yes/No) 🍃 Leaf Nodes → Final output 🔹 3. Example 👉 Predict if a person will buy a product: Is Age > 30? ├── Yes → High Chance └── No → Check Income ├── High → Medium Chance └── Low → Low Chance 🔹 4. Types of Problems ✔ Classification (Yes/No) ✔ Regression (predict values) 🔹 5. Implementation (Python) from sklearn.tree import DecisionTreeClassifier # Sample data X = [[25], [30], [45], [50]] y = [0, 0, 1, 1] model = DecisionTreeClassifier() model.fit(X, y) print(model.predict([[40]])) 🔹 6. Advantages ⭐ ✔ Easy to understand ✔ No need for scaling ✔ Works with both numbers & categories 🔹 7. Disadvantages ❌ Can overfit (too complex tree) ❌ Sensitive to small data changes 🔹 8. Why Decision Trees are Important? ✔ Used in real-world ML systems ✔ Foundation for Random Forest & XGBoost ✔ Easy to explain to stakeholders 🎯 Today’s Goal ✔ Understand tree structure ✔ Learn splitting logic ✔ Implement basic model 💬 Tap ❤️ for more!

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75 717

What does a threshold (0.5) do?

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75 717

Which function is used in Logistic Regression?

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75 717

What is the range of output in Logistic Regression?

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75 717

Logistic Regression is used for which type of problem?

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