01. What does a stratified train test split ensure?
a) The split is random without any additional conditions.
b) The positive to negative cases ratio is the same in train and test sets.
c) The train and test sets have the same absolute number of positive cases.
d) The positive to negative cases ratio in the train set is as close to 1 as possible.
02. Which statement is true about a categorical feature that has 998 unique values in a dataset of 1000 records?
a) Unless some categories could be split, this feature should be excluded from the model since some of its values are not unique.
b) This feature could be included into the model without any transformations since it has enough categories relative to the total number of records.
c) Unless some categories could be grouped, this feature should be excluded from the model since it has too many categories relative to the total number of records.
d) This feature could be included into the model since the number of rows with non - unique values is small, which means they could be safely deleted during preprocessing.
03. What is the standard approach in evaluating the performance of classification models using supervised machine learning?
a) Confusion matrix
b) Cross-validation
c) Regularization
d) Principal component analysis (PCA)
04. How should k-fold cross validation be performed when data is split into test and train sets?
a) Split the train set on k folds. Train the model on each fold and validate on test data.
b) Split the train set on k folds. For each fold perform training using other k-1 folds and validate on this fold.
c) Split the entire dataset on k folds. For each fold train the model on this fold and validate on the rest k-1 folds.
d) Split the entire dataset on k folds. For each fold perform training using other k-1 folds and validate on the entire dataset.
05. When is z-score normalization most useful?
a) When the data falls between 0 and 1.
b) When the data contains many outliers.
c) When the original range of the data needs to be retained.
d) When data is normally distributed.
06. Which statement describes sample variance and standard deviation?
a) Variance is the square root of the average squared deviations from the mean, while standard deviation is the sum of these squared deviations.
b) Variance is the average distance of each data point from the mean, while standard deviation is the total distance of all points from the mean divided by the number of points.
c) Variance is the average of all squared differences from the mean, while standard deviation is the square root of the variance, showing how spread out the data is from the mean.
d) Variance measures the total variability within a dataset without normalization, while standard deviation is calculated by dividing the variance by the number of data points minus one.
07. If the goal of the model is to predict whether a user will churn, which feature will cause data leakage if included in the training set?
a) Subscribe date
b) Unsubscribe date
c) Last payment date
d) Last suspension date
08. What is the repository of statistic libraries called that is used by the R programming language?
a) scikit-learn
b) R Package Manager (RPM)
c) MLlib (Machine Learning library)
d) Comprehensive R Archive Network (CRAN)
09. Which statement describes covariance?
a) A measure of association between two variables.
b) A measure of model performance.
c) A measure of the distribution of values within a variable.
d) A measure of overall predictive power of a set of variables.
10. A customer needs a model to identify fraud by flagging transactions with either a 0 or 1. Which type of model should they choose?
a) k-means
b) Linear Regression
c) Logistic Regression
d) Multi-class Decision Tree
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