Logistic Corruption estimates the anticipation of bifold aftereffect as a action of independent variables.
An archetype is the anticipation that a borrower will absence as a action of his acclaim score,
income, loan admeasurement and his accepted debts. In another words, Logistic Corruption is a statistical method
for allegory dataset in which there are one or added absolute variables (predictor) that determine
the aftereffect of a dichotomous dependent capricious such as YES or NO. Dichotomous is an aftereffect variable
with the achievability of alone two another outcomes such as TRUE or FALSE.
Binary logistic corruption archetypal is acclimated to appraisal the anticipation of the bifold acknowledgment of dependent
variable based on one or added absolute or augur variables. Logistic corruption uses categorical
predictor to adumbrate the bifold abased outcome.
In assessing the predictive ability of absolute predictors of a bifold outcome,
should logistic corruption be used?
Another way to anatomy the catechism is:
Can logistic corruption be acclimated to predict
If yes, again how can logistic regression be acclimated to predict
If no, why?
Start by defining Logistic regression
Define bifold logistic regression.
Describe the predictive ability of categorical capricious on bifold outcome.
Explain the account of logistic corruption in Big Data Analytics.
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