1. Question 1 Type of Statistics in which the posterior probability is the updated belief on the probability of an event happening given the prior and the data observed. 1 / 1 point…
2. Question 2 Type 1 Error 1 is defined as: 1 / 1 point Saying the null hypothesis is false, when it is actually true Saying the null hypothesis is true, when…
1. Question 1 A p-value is: 1 / 1 point the smallest significance level at which the null hypothesis would be rejected the probability of the null hypothesis being true the probability…
1. Question 1 (True/False) In general, the population parameters are unknown 1 point True. False. ———————————————————————————————————- 2. Question 2 (True/False) Parametric models have finite number of parameters. 1…
1. Question 1 Which of the following statements about cloud data access using Pandas is TRUE? 1 / 1 point With read_csv , the online file must be comma-delimited. The ead_csv function…
3. Question 3 Which of these approaches to feature engineering will be impacted LEAST by extreme values? 1 point RobustScaler MinMaxScaler LabelBinarizer OneHotEncoder ———————————————————————————————————- 8. Question 8 Which…
4. Question 4 Which of these approaches to feature engineering will be impacted MOST by extreme values? 1 / 1 point RobustScaler MinMaxScaler LabelBinarizer OneHotEncoder ———————————————————————————————————- 7. Question 7 (True/False)…
2. Question 2 In which case below is it most plausible to conclude that an observation includes an outlier for one of the features? 1 / 1 point One feature has a…
1. Question 1 (True/False) Classification models require that input features be scaled. 1 point True False ———————————————————————————————————- 2. Question 2 (True/False) Feature scaling allows better interpretation of distance-based…
1. Question 1 From the options listed below, select the option that is NOT a valid exploratory data approach to visually confirm whether your data is ready for modeling or…
