RR 7: AutoML Binary Classification Pipeline Evaluation
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Questions and Answers

According to the authors, which data quality issue can AutoML systems handle effectively?

  • Duplicates
  • Missing values
  • Outliers (correct)
  • Inconsistencies

    • What did the authors use synthetic errors for in their study?

  • To evaluate the ability of AutoML systems
  • To characterize the correlation between ML-models performance and data quality (correct)
  • To introduce noise into the training data
  • To enhance the cleaning of benchmark datasets

    • What is the main focus of Frénay and Verleysen's literature survey?

  • Label noise in test data (correct)
  • Synthetic errors in ML-models
  • Cleaning benchmark datasets
  • Effect of label noise on ML-benchmark results

    • What do Northcutt et al. emphasize about label noise in test data?

    <p>It leads to favoring simpler models (D)</p> Signup and view all the answers

    What does the degree of inconsistency of a feature measure according to Definition 1?

    <p>The ratio of replacement operations required to transform it into a consistent state (C)</p> Signup and view all the answers

    How is pollution introduced into a dataset for the consistent representation dimension?

    <p>By generating new representations for each unique value of a pollutable feature (D)</p> Signup and view all the answers

    What is the primary focus of the research described in the text?

    <p>Investigating the relationship between data quality and ML algorithm performance (B)</p> Signup and view all the answers

    Which factor can lead to unreliable models, according to the text?

    <p>Incomplete or erroneous training data (B)</p> Signup and view all the answers

    What is emphasized as a requirement for trustworthy AI applications?

    <p>High-quality training and test data (B)</p> Signup and view all the answers

    In what three tasks do the ML algorithms studied in the research specialize?

    <p>Classification, regression, and clustering (C)</p> Signup and view all the answers

    What are the three scenarios distinguished in the research based on the AI pipeline steps fed with polluted data?

    <p>Polluted training data, test data, or both (D)</p> Signup and view all the answers

    What is the main conclusion of the research?

    <p>The performance of ML algorithms can be explained in terms of data quality dimensions (B)</p> Signup and view all the answers

    What is the ultimate aim of the study mentioned in the text?

    <p>To understand ML model behavior in terms of data quality (D)</p> Signup and view all the answers

    What is the main focus of the paper mentioned in the text?

    <p>The relation between data quality dimensions and ML-model performance (C)</p> Signup and view all the answers

    What led to a shift in research focus from a model-centric approach to a data-centric approach for building AI systems?

    <p>The enormous growth of data and its challenges (C)</p> Signup and view all the answers

    What is the contribution of the paper discussed in the text?

    <p>Insights on data quality in ML-pipelines (A)</p> Signup and view all the answers

    What is a potential challenge posed by AI-based systems in enterprises, as discussed in the text?

    <p>Data life cycle concerns (A)</p> Signup and view all the answers

    What does the completeness of a feature measure?

    <p>The ratio of missing values to the total number of samples in the dataset (B)</p> Signup and view all the answers

    Which approach is used for data validation in ML pipelines, as mentioned in the text?

    <p>Unit tests focusing on data consistency and completeness (A)</p> Signup and view all the answers

    What are the three scenarios considered in the study for varying data quality?

    <p>High-quality training data, low-quality testing data; high-quality testing data, low-quality training data; same quality training and testing data (B)</p> Signup and view all the answers

    What does a completeness of 1 for a dataset indicate?

    <p>No features have missing values (D)</p> Signup and view all the answers

    Why is a placeholder representation considered as pollution?

    <p>Because placeholders do not contain information related to the data and have no reconstruction involved (D)</p> Signup and view all the answers

    According to the text, what does Foroni et al. argue about data quality assessment in relation to the task at hand?

    <p>It should not be performed in isolation from the task at hand (B)</p> Signup and view all the answers

    What aspect of the ML-pipeline does the text mention as playing a different role at different stages?

    <p>Data usage (C)</p> Signup and view all the answers

    What does the feature accuracy measure for a categorical feature?

    <p>The number of values in the feature that are different from the ground truth (A)</p> Signup and view all the answers

    What did the researchers highlight as challenges in the context of building 'data ecosystems' in enterprises?

    <p>Data quality issues (B)</p> Signup and view all the answers

    What did Li et al. investigate regarding the impact of data cleaning on classification algorithms?

    <p>The influence of cleaning training data on classification performance (B)</p> Signup and view all the answers

    What is the average feature accuracy measure of all numerical features called?

    <p>nFAccuracy (B)</p> Signup and view all the answers

    What are some of the error types focused on by Li et al. during their investigation?

    <p>Outliers, duplicates, in-consistencies, and mislabels (A)</p> Signup and view all the answers

    Why do ML-models exclude samples with a missing value for the target feature from the dataset?

    <p>Because they require complete datasets for training (C)</p> Signup and view all the answers

    What is the target accuracy equation for a categorical target feature?

    <p>$cTAccuracy(d) = 1 - mismatches(target) / n$ (B)</p> Signup and view all the answers

    What does the level of pollution λfa for a categorical feature determine?

    <p>The percentage of samples to be polluted (B)</p> Signup and view all the answers

    How is pollution executed for numeric features?

    <p>By adding normally distributed noise to all samples of the feature (D)</p> Signup and view all the answers

    What is the uniqueness metric used to evaluate?

    <p>Performance of ML-models (A)</p> Signup and view all the answers

    What does the target accuracy equation for a numerical target feature measure?

    <p>The average sum of the absolute distances of the ground truth and target feature values (A)</p> Signup and view all the answers

    In de-duplication process, what is considered as duplicates in practice?

    <p>All of the above (D)</p> Signup and view all the answers

    What does the level of pollution λfa for a numeric feature determine?

    <p>The level of noise to be added to all samples (A)</p> Signup and view all the answers

    What does the target accuracy equation for a categorical target feature measure?

    <p>$cTAccuracy(d) = 1 - mismatches(target) / n$ (A)</p> Signup and view all the answers

    What is the primary purpose of de-duplication in ML pipelines?

    <p>To avoid overfitting in ML-models (A)</p> Signup and view all the answers

    What does λta represent in pollution for numerical targets?

    <p>Variance of normally distributed noise (B)</p> Signup and view all the answers

    What area has seen recent enormous growth that has enhanced the potential for AI?

    <p>Data management (D)</p> Signup and view all the answers

    What is the ultimate aim of the study mentioned in the text?

    <p>To understand ML model behavior in terms of data quality (C)</p> Signup and view all the answers

    What do researchers point out as challenges in the context of building 'data ecosystems'?

    <p>All of the above (D)</p> Signup and view all the answers

    What is considered to be a different role at different stages of the ML-pipeline?

    <p>Training data in ML-pipeline (A)</p> Signup and view all the answers

    What is the primary focus of the research described in the text?

    <p>Studying the impact of data quality dimensions on machine learning performance (B)</p> Signup and view all the answers

    According to the authors, which data quality issue can AutoML systems handle effectively?

    <p>Missing values (A)</p> Signup and view all the answers

    What does the level of pollution λfa for a numeric feature determine?

    <p>Feature accuracy (A)</p> Signup and view all the answers

    What is emphasized as a requirement for trustworthy AI applications?

    <p>Validation of serving data (D)</p> Signup and view all the answers

    What is the main focus of the study mentioned in the text?

    <p>Exploring the performance of machine learning algorithms (B)</p> Signup and view all the answers

    What is emphasized as a requirement for trustworthy AI applications?

    <p>Completeness of training data (A)</p> Signup and view all the answers

    What factor can lead to unreliable models, according to the text?

    <p>Erroneous training data (A)</p> Signup and view all the answers

    What are the three scenarios distinguished in the research based on the AI pipeline steps fed with polluted data?

    <p>Polluted training data, polluted test data, or both (A)</p> Signup and view all the answers

    What does the degree of consistency of a feature measure according to Definition 1?

    <p>The ratio of replacement operations to transform it into a consistent state (A)</p> Signup and view all the answers

    What does λcr represent in pollution for categorical features?

    <p>The percentage of samples to be polluted (A)</p> Signup and view all the answers

    What is the main focus of Frénay and Verleysen's literature survey?

    <p>The effect of label noise on ML-benchmark results (D)</p> Signup and view all the answers

    What does the problem of missing values represent in datasets according to the text?

    <p>Values that are actually missing (A)</p> Signup and view all the answers

    What does the completeness of a feature measure?

    <p>The ratio of missing values to total samples in the dataset (D)</p> Signup and view all the answers

    What does the level of pollution λfa for a categorical feature determine?

    <p>The degree of inconsistency of the feature (B)</p> Signup and view all the answers

    What did Li et al. investigate regarding the impact of data cleaning on classification algorithms?

    <p>The trade-offs between model accuracy and model complexity (C)</p> Signup and view all the answers

    What is the feature accuracy measure for a categorical feature?

    <p>The ratio of mismatched values to total samples in the dataset (C)</p> Signup and view all the answers

    What does the feature accuracy quality measure of all numeric features nF Accuracy represent?

    <p>The average of all per-feature accuracies (C)</p> Signup and view all the answers

    What is the target accuracy equation for a categorical target feature?

    <p>$1 - mismatches(target) / n$ (C)</p> Signup and view all the answers

    What is the pollution introduced into a dataset for the consistent representation dimension?

    <p>Adding normally distributed noise to all samples of the feature (C)</p> Signup and view all the answers

    What does the uniqueness metric used to evaluate represent?

    <p>The deviation of target feature values from their respective ground truth values (D)</p> Signup and view all the answers

    What does λfa represent in pollution for numerical targets?

    <p>The standard deviation of the normal distribution for numeric features (D)</p> Signup and view all the answers

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