What is Random forest?Random forest kya hai?

🧸 Think of it likeAise samjho

Like asking 500 people for directions and going with the majority: one person may be wrong, the crowd usually isn't.500 logon se raasta poochh kar majority ki baat maanna: ek insaan galat ho sakta hai, bheed aksar nahi.

📘 ExplainedVistaar se

Each tree sees a random subset of rows and features, so trees make different mistakes, and averaging cancels many of them out. Random forests are robust, need little tuning and are a strong baseline for tabular data; gradient boosting is often a bit more accurate.Har tree rows aur features ka random hissa dekhta hai, isliye trees alag galtiyaan karte hain, aur average karne se kai galtiyaan cancel ho jaati hain. Random forests mazboot hain, kam tuning chahiye aur tabular data ke liye accha baseline hain; gradient boosting aksar thoda zyaada accurate hota hai.

💡 Real exampleAsli example

500 trees voting on whether a customer will churn.500 trees ka vote ki customer churn karega ya nahi.

📖 Learn it step by stepStep by step seekho

• Decision trees, random forests and boostingDecision trees, random forests aur boosting

🔗 Related termsJude hue shabd

• Decision treeA model that predicts by asking a series of yes or no questions about the input.Model jo input ke baare mein haan ya nahi sawaalon ki ek series poochh kar predict karta hai.
• Gradient boostingBuilding many small decision trees one after another, each fixing the previous ones' mistakes.Kai chhote decision trees ek ke baad ek banana, har ek pichhlon ki galtiyaan sudhaarta hai.