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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To that end, when files are actively downloading, a new icon now appears in the Search bar that users can tap to view and manage downloads, pause and resume all downloads or just individual items, and select one to increase its priority or view it in a chat. 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. On Telegram’s website, it says that Pavel Durov “supports Telegram financially and ideologically while Nikolai (Duvov)’s input is technological.” Currently, the Telegram team is based in Dubai, having moved around from Berlin, London and Singapore after departing Russia. Meanwhile, the company which owns Telegram is registered in the British Virgin Islands. Telegram has become more interventionist over time, and has steadily increased its efforts to shut down these accounts. But this has also meant that the company has also engaged with lawmakers more generally, although it maintains that it doesn’t do so willingly. For instance, in September 2021, Telegram reportedly blocked a chat bot in support of (Putin critic) Alexei Navalny during Russia’s most recent parliamentary elections. Pavel Durov was quoted at the time saying that the company was obliged to follow a “legitimate” law of the land. He added that as Apple and Google both follow the law, to violate it would give both platforms a reason to boot the messenger from its stores. The War on Fakes channel has repeatedly attempted to push conspiracies that footage from Ukraine is somehow being falsified. One post on the channel from February 24 claimed without evidence that a widely viewed photo of a Ukrainian woman injured in an airstrike in the city of Chuhuiv was doctored and that the woman was seen in a different photo days later without injuries. The post, which has over 600,000 views, also baselessly claimed that the woman's blood was actually makeup or grape juice.
from nl
