Содержание
- 2. Logistic Regression is a statistical method of classification of objects. In this tutorial, we will focus
- 3. A doctor classifies the tumor as malignant or benign. A bank transaction may be fraudulent or
- 4. Logistic Regression is just one part of machine learning used for solving this kind of binary
- 5. There are other classification problems in which the output may be classified into more than two
- 9. The sigmoid function also known as the logistic function is going to be the key to
- 10. We can take our linear regression solution and place it into the sigmoid function and it
- 11. We can set a cutoff point at 0.5and we can say anything below 0.5 results in
- 13. Convex cost function for logistic regression •If h goes to zero and Cost also goes to
- 14. Model evaluation After we have trained a logistic regression model on some training dataset we can
- 15. #example: testing the presence of a disease NO = negative test = False = 0 YES
- 16. Misclassification Rate: how often is it wrong? MR = (FP+FN)/total MR = (10+5)/165 = 0.09 This
- 18. Multi-class classification One-vs-all strategy: working with multiple binary classifications We train one logistic regression classifier for
- 20. How to deal with overfitting Seems having higher order of polynomials is good fit, but how
- 21. Advantages: it doesn’t require high computational power is easily interpretable is used widely by the data
- 22. Disadvantages: while working with Logistic regression you are not able to handle a large number of
- 23. https://www.youtube.com/watch?v=yIYKR4sgzI8
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