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👋Day 2 #31DayofML
Let's explore #MachineLearning terms for supervised learning:
🔸Labels - the thing we're predicting
🔸Features - an input variable
🔸Examples - particular instance of data (Labeled/Unlabeled)
🔸Models - defines the relationship between features & label.
A 🧵
Let's explore #MachineLearning terms for supervised learning:
🔸Labels - the thing we're predicting
🔸Features - an input variable
🔸Examples - particular instance of data (Labeled/Unlabeled)
🔸Models - defines the relationship between features & label.
A 🧵
🔸Labels - the thing we're predicting
Eg: The y variable in simple linear regression. The label could be the future price of wheat, the kind of animal shown in a picture, the meaning of an audio clip, or just about anything.
#31DayofML
Eg: The y variable in simple linear regression. The label could be the future price of wheat, the kind of animal shown in a picture, the meaning of an audio clip, or just about anything.
#31DayofML
🔸Features - an input variable x in linear regression.
A simple ML project might use 1 feature X, while a sophisticated project could use millions of features X1, X2, ..... Xn
Eg: In the spam detector, features could be:
📌Words in email
📌Sender's address
📌Time
📌Some phrase
A simple ML project might use 1 feature X, while a sophisticated project could use millions of features X1, X2, ..... Xn
Eg: In the spam detector, features could be:
📌Words in email
📌Sender's address
📌Time
📌Some phrase
