is "multivariable linear regression" the same as logistic regression?

I am new to machine learning and I am simultaneously studying linear and logistic regression. Logistic regression is when there is one dependent variable and there may be more than one independent variable

Multivariable linear regression attempts to find a linear relationship between multiple variables where every independent variable x must have a dependent variable y.

Can anyone tell me the difference between the two?

• Logistic regression involves a binary response. Linear regression does not. Commented Oct 19, 2019 at 0:49

In linear regression, you predict the target variable $$y$$ using a model that is based on a linear function of the predictors $$X_1,X_2,\dots,X_k$$

$$y = \beta_0 + \beta_1 X_2 + \beta_2 X_2 + \dots + \beta_k X_k + \varepsilon$$

In logistic regression, the target variable is binary, and you are trying to predict probability of "success" $$p(y=1)$$ using a linear predictor $$\eta$$ passed through the link function $$\sigma$$ (logistic function is the most common choice), i.e.

$$\eta = \beta_0 + \beta_1 X_2 + \beta_2 X_2 + \dots + \beta_k X_k \\ p(y=1) = \sigma(\eta)$$

So both can be functions of a single, or multiple variables. The differences are that the target variable differs (values on real line vs binary), and logistic regression, as other generalized linear models, uses a link function.

In multiple linear regression (MLR), you still have one dependent variable, and the relation is as follows: $$y_i=\beta_0+\sum_{i=1}^n\beta_ix_i+\epsilon$$ In logistic regression (LR), what you predict is a probability, namely the class posterior, and is bounded unlike MLR output. They're similar in the sense that they both use linear combination of features. The probability of class 1 is estimated as follows in LR: $$p_i=\frac{1}{1+e^{-(\beta_0+\sum\beta_i x_i)}}$$

So, LR is actually taking $$\text{logit}$$ of MLR output.