Imagine you're trying to figure out what to eat for dinner. 🍕🥗🍔 A decision tree is like a flowchart that helps you make choices based on yes/no questions:
Are you in the mood for something light? Yes ➡️ Salad 🥗 No ➡️ Are you craving something cheesy? Yes ➡️ Pizza 🍕 No ➡️ Burger 🍔
That's the essence of how decision trees work in machine learning!
🤖 In Machine Learning Terms:
Nodes: Questions (e.g., Is the price > $50?) Branches: Possible answers (e.g., Yes/No) Leaves: Final decisions or predictions (e.g., "Expensive" or "Affordable")
📊 They're used for tasks like: ✅ Classifying emails as spam or not. ✅ Predicting if a customer will buy a product. ✅ Diagnosing diseases in healthcare.
🎯 Why are they Awesome?
Simple to understand (even for non-techies). Visual and interpretable (you can see the logic behind predictions). Great for small-to-medium datasets.
⚡️ Limitations:
They can "overfit" (become too specific). Not the best for very large datasets or complex problems.
🛠Pro Tip: To handle overfitting, use Random Forests 🌲🌲 or Gradient Boosted Trees 🚀—advanced versions of decision trees.
What do you think about decision trees? Drop your 🌳 below if you love their simplicity!
Imagine you're trying to figure out what to eat for dinner. 🍕🥗🍔 A decision tree is like a flowchart that helps you make choices based on yes/no questions:
Are you in the mood for something light? Yes ➡️ Salad 🥗 No ➡️ Are you craving something cheesy? Yes ➡️ Pizza 🍕 No ➡️ Burger 🍔
That's the essence of how decision trees work in machine learning!
🤖 In Machine Learning Terms:
Nodes: Questions (e.g., Is the price > $50?) Branches: Possible answers (e.g., Yes/No) Leaves: Final decisions or predictions (e.g., "Expensive" or "Affordable")
📊 They're used for tasks like: ✅ Classifying emails as spam or not. ✅ Predicting if a customer will buy a product. ✅ Diagnosing diseases in healthcare.
🎯 Why are they Awesome?
Simple to understand (even for non-techies). Visual and interpretable (you can see the logic behind predictions). Great for small-to-medium datasets.
⚡️ Limitations:
They can "overfit" (become too specific). Not the best for very large datasets or complex problems.
🛠Pro Tip: To handle overfitting, use Random Forests 🌲🌲 or Gradient Boosted Trees 🚀—advanced versions of decision trees.
What do you think about decision trees? Drop your 🌳 below if you love their simplicity!
BY Data science/ML/AI
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As a result, the pandemic saw many newcomers to Telegram, including prominent anti-vaccine activists who used the app's hands-off approach to share false information on shots, a study from the Institute for Strategic Dialogue shows. However, the perpetrators of such frauds are now adopting new methods and technologies to defraud the investors. "Your messages about the movement of the enemy through the official chatbot … bring new trophies every day," the government agency tweeted. The company maintains that it cannot act against individual or group chats, which are “private amongst their participants,” but it will respond to requests in relation to sticker sets, channels and bots which are publicly available. During the invasion of Ukraine, Pavel Durov has wrestled with this issue a lot more prominently than he has before. Channels like Donbass Insider and Bellum Acta, as reported by Foreign Policy, started pumping out pro-Russian propaganda as the invasion began. So much so that the Ukrainian National Security and Defense Council issued a statement labeling which accounts are Russian-backed. Ukrainian officials, in potential violation of the Geneva Convention, have shared imagery of dead and captured Russian soldiers on the platform. Oh no. There’s a certain degree of myth-making around what exactly went on, so take everything that follows lightly. Telegram was originally launched as a side project by the Durov brothers, with Nikolai handling the coding and Pavel as CEO, while both were at VK.
from ar
