Lec 12 Logistic Regression in 14 minutes

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1) Why Can't we use linear regression for classification ?
2) What is a logistic function and how it is suited in classification ?
3) What are we regressing in logistic regression is it the probability or something else ?
4) What is the loss function we try to maximize ?
5) How to extend to multi class
  1. Saptarsi Goswami
  2. ml
  3. machine learning
  4. data science
  5. classification
  6. logistic regression
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1) Linear Regression can't be applied because the value of the response variable can be less than 0, or, greater than 1 and hence we cannot strictly restrict the values between 0 to 1.
2)Sigmoid function is suited for classification as the value of the function ranges between 0-1 as desired for logistic regression.
3)We are regressing the response variable (may be).
4)We try to maximize the coefficients beta0, beta1.
5)In multi-class class, classification we would try to find the individual probability of each class, then, the largest of all these values dominates and becomes the predicted class.
Lastly thanks for the wonderful MLE example, Sir.

arghyakusumdas