Why do we use logistic regression and not linear regression?
The Differences between Linear Regression and Logistic Regression. Linear Regression is used to handle regression problems whereas Logistic regression is used to handle the classification problems. Linear regression provides a continuous output but Logistic regression provides discreet output.
How logistic regression works?
Logistic regression uses an equation as the representation, very much like linear regression. Input values (x) are combined linearly using weights or coefficient values (referred to as the Greek capital letter Beta) to predict an output value (y).
Why can’t we use linear regression for the classification problem?
There are two things that explain why Linear Regression is not suitable for classification. The first one is that Linear Regression deals with continuous values whereas classification problems mandate discrete values. The second problem is regarding the shift in threshold value when new data points are added.
Which is the correct explanation of logistic regression?
A simpler intuitive explanation. Contrary to its name logistic regression is a classification algorithm. Given an input example, a logistic regression model assigns the example to a relevant class. A note on the notation. x_ {i} means x subscript i and x_ {^th} means x superscript th.
Do you need a threshold value for logistic regression?
Ideally, one would want a threshold value that balances sensitivity and specificity however, the value of threshold is problem specific and depending on how tolerant you want the model to be, the value has to tuned. Consider a classification problem with three arbitrary classes represented in the plot below —
How does John’s logistic regression model predict spam?
John’s logistic regression model. For an unseen example falling inside the circle (decision boundary) the equation of circle returns a negative value therefore the above model returns a value less than 0.5, implying the probability of that example being spam is less than 0.5.
Is the contour of a logistic regression closed?
First we try the logistic regression without regularization. As we can see with the data moving apart, the objective function (logistic loss) is changing dramatically, and the optim is moving away to a larger value. When we have completed the operation, the contour will not be a “closed shape”.