- Deep Learning By Example
- Ahmed Menshawy
- 173字
- 2021-06-24 18:52:41
Simple regression model
The linear regression model is a learning algorithm that is concerned with predicting a quantitative (also known as numerical) response using a combination of explanatory features (or inputs or predictors).
A simple linear regression model with only one feature takes the following form:
y = beta0 + beta1x
Here:
- y is the predicted numerical value (response) → sales
- x is the the feature
- beta0 is called the intercept
- beta1 is the coefficient of the feature x → TV ad
Both beta0 and beta1 are considered as model coefficients. In order to create a model that can predict the value of sales in the advertising example, we need to learn these coefficients because beta1 will be the learned effect of the feature x on the response y. For example, if beta1 = 0.04, it means that an additional $100 spent on TV ads is associated with an increase in sales by four widgets. So, we need to go ahead and see how can we learn these coefficients.
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