DSCI 4520 Quiz 4 Questions Answered Correctly Graded A+

DSCI 4520 Quiz 4 Questions Answered Correctly Graded A+ One of the advantages of naive Bayes classification models is the ease of training and interpreting. - Answers True If events A and B are statistically independent, what is P(A|B), that is the conditional probability of A, given B? - Answers P(A) Statistical independence for two events is present when the outcome of the first event has no impact on the probability of the second event - Answers True With the Naive Bayes classification method, the zero frequency problem occurs if a given scenario for a single predictor has not been observed. - Answers True Which statement is INCORRECT about Naïve Bayes classifier? - Answers It computes and includes prior probability of predictors Which one is NOT one of the advantages of the Naive Bayes classifiers? - Answers Assumption of independence of features What is the predicted variable in the logistic regression model? - Answers Probability of class membership Which of the following statements is INCORRECT about the logistic regression model? - Answers In the logistic regression, the intercept cannot be zero because of the natural logarithm function Logistic regression is a more complex model than the naive Bayes and it typically takes more computational resources to train the model. - Answers True Similar to linear regression, the search for the optimized set of input features for a logistic regression model can be done by the greedy (exhaustive) algorithm. - Answers True Which statement explains the issues when linear regression is used to model binary target variables? - Answers Predicted probabilities can be 1 or 0 leading to model interpretation difficulties The target variable of logistic regression can be numerical. - Answers False How can we turn the logistic regression model into the classification model? - Answers By setting a cutoff value and comparing the predicted probability with it In the logistic regression model the target variable is: - Answers A categorical variable Input variables (features) of the logistic regression model can be categorical or numerical. - Answers True Which statement is correct about the cutoff value of the probability calculated by a logistic regression model to be used for classification? - Answers The cutoff value is an arbitrary value determined by model performance assessment The following chart shows the prediction error of a decision tree based on the training set and validation set as functions of the number of splits. What phenomenon is causing the gap between the two curves at higher numbers of splits? - Answers Model overfitting