Скачать с ютуб What is AIC and BIC | Data Science Interview Questions and Answers | Thinking Neuron в хорошем качестве

What is AIC and BIC | Data Science Interview Questions and Answers | Thinking Neuron

AIC: The Akaike Information Criterion (AIC) provides a method for assessing the quality of your predictive model through comparison of related models. The number itself is not meaningful. If you have more than one model then you should select the model that has the smallest AIC. AIC is used in Logistic Regression to perform goodness of fit test since there is no R2 for Logistic Regression. AIC is computed using below equation AIC = -2 * log-likelihood + K * nPar log-likelihood: The log-likelihood of logistic regression K : 2 nPar: Number of columns ################### What is BIC? The formula for the Bayesian information criterion (BIC) is similar to the formula for AIC, but with a different penalty for the number of parameters. Unlike the AIC, the BIC penalizes free parameters more strongly. BIC is computed using the below equation which is the same as AIC. The only difference is the value of K which is used in BIC. AIC = -2 * log-likelihood + K * nPar log-likelihood: The log-likelihood of logistic regression K : log(number of Rows) nPar: Number of columns #datascience #datascienceinterview #datascienceinterviewquestions #machinelearning #logisticregression Learn more at the Thinking Neuron Website https://thinkingneuron.com/data-scien...