Скачать или смотреть 📚 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐯𝐬 𝐕𝐚𝐥𝐢𝐝𝐚𝐭𝐢𝐨𝐧 𝐯𝐬 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 — 𝐓𝐡𝐞 𝐓𝐡𝐫𝐞𝐞 𝐃𝐚𝐭𝐚 𝐒𝐩𝐥𝐢𝐭𝐬 𝐘𝐨𝐮 𝐃𝐨𝐧’𝐭 𝐌𝐢𝐱 📚

📚 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐯𝐬 𝐕𝐚𝐥𝐢𝐝𝐚𝐭𝐢𝐨𝐧 𝐯𝐬 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 — 𝐓𝐡𝐞 𝐓𝐡𝐫𝐞𝐞 𝐃𝐚𝐭𝐚 𝐒𝐩𝐥𝐢𝐭𝐬 𝐘𝐨𝐮 𝐃𝐨𝐧’𝐭 𝐌𝐢𝐱 📚

A Machine Learning model can’t be evaluated on the same data it learned from — that tells you nothing about how it performs in the real world.
That’s why every dataset is split into 𝐭𝐡𝐫𝐞𝐞 𝐝𝐢𝐬𝐭𝐢𝐧𝐜𝐭 𝐩𝐚𝐫𝐭𝐬, each with a different job.

🧪 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐒𝐞𝐭
Where the model 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐥𝐞𝐚𝐫𝐧𝐬.
It adjusts its parameters by minimizing error across thousands or millions of examples.
This is pattern-absorption mode.

📉 𝐕𝐚𝐥𝐢𝐝𝐚𝐭𝐢𝐨𝐧 𝐒𝐞𝐭
A separate slice of data used to 𝐭𝐮𝐧𝐞 𝐡𝐲𝐩𝐞𝐫𝐩𝐚𝐫𝐚𝐦𝐞𝐭𝐞𝐫𝐬, check progress, prevent overfitting, and decide when to stop training.
It’s the model’s “awkward teen phase” — 𝐢𝐦𝐩𝐫𝐨𝐯𝐢𝐧𝐠 𝐰𝐢𝐭𝐡𝐨𝐮𝐭 𝐜𝐡𝐞𝐚𝐭𝐢𝐧𝐠 𝐛𝐲 𝐩𝐞𝐞𝐤𝐢𝐧𝐠 𝐚𝐭 𝐭𝐡𝐞 𝐟𝐢𝐧𝐚𝐥 𝐚𝐧𝐬𝐰𝐞𝐫𝐬.

🎓 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 𝐒𝐞𝐭
The final exam.
Completely untouched during training and validation.
𝐈𝐭 𝐦𝐞𝐚𝐬𝐮𝐫𝐞𝐬 𝐡𝐨𝐰 𝐰𝐞𝐥𝐥 𝐭𝐡𝐞 𝐦𝐨𝐝𝐞𝐥 𝐠𝐞𝐧𝐞𝐫𝐚𝐥𝐢𝐳𝐞𝐬 𝐭𝐨 𝐭𝐫𝐮𝐥𝐲 𝐮𝐧𝐬𝐞𝐞𝐧 𝐝𝐚𝐭𝐚.

💡𝐈𝐧 𝐬𝐡𝐨𝐫𝐭:
— 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 → 𝐋𝐞𝐚𝐫𝐧
— 𝐕𝐚𝐥𝐢𝐝𝐚𝐭𝐢𝐨𝐧 → 𝐓𝐮𝐧𝐞 + 𝐜𝐡𝐞𝐜𝐤 𝐨𝐯𝐞𝐫𝐟𝐢𝐭𝐭𝐢𝐧𝐠
— 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 → 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐞 𝐫𝐞𝐚𝐥 𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞

Mix them up and you get a model that thinks it’s a genius but collapses the moment reality shows up.

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