Thread by Sasi Sekaran: How to identify a Linear Regression Problem in ML? Use these 3 simple techniques /๐งต/ twitter.com/freest_man/status/1610624097628684294/photo/1
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 Jan 4, 2023
 #Math #MachineLearning
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1/ Linear relationship
Look for a linear relationship between the independent (predictor) variables and the dependent (outcome) variable.
Ex: The price of a house has a linear relationship to the size (sq ft) of the house.
When the size increases the price also increases.
Look for a linear relationship between the independent (predictor) variables and the dependent (outcome) variable.
Ex: The price of a house has a linear relationship to the size (sq ft) of the house.
When the size increases the price also increases.
2/ Continous Outcome Variable
The outcome variable is continuous (i.e., a numeric value that can take on any value within a certain range)
Ex: Predicting the stock price (Continous Outcome Variable) of a company based on the economical indicators.
The outcome variable is continuous (i.e., a numeric value that can take on any value within a certain range)
Ex: Predicting the stock price (Continous Outcome Variable) of a company based on the economical indicators.
3/ No clear pattern
Another way to identify a linear regression problem is to look for a lack of clear patterns or relationships between the variables.
If your dataset has no sure relationship between the variables, a regression can be a good way to understand the relationships
Another way to identify a linear regression problem is to look for a lack of clear patterns or relationships between the variables.
If your dataset has no sure relationship between the variables, a regression can be a good way to understand the relationships
That's a wrap!
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 Mar 23, 2023
Great Thread Sasi!