29 Questions
What is the impact of missing data on models according to the text?
Reduced accuracy as the model is trained on an incomplete representation.
What is one potential reason for missing data related to technical challenges?
Malfunctioning sensors due to sensitive topics like income or health.
How does missing data affect model training when it is not random?
Leads to biased models that misrepresent the underlying population or phenomena.
What could be a consequence of censoring in the context of missing data?
Creation of non-representative study samples.
Which factor can contribute to missing data occurrence based on human factors?
Individuals choosing not to answer all questions in a survey.
What is the purpose of using a Naive approach in problem-solving?
To simplify the problem by making unrealistic assumptions
Which dataset contains images of 50 different cities with dense annotations grouped into 8 categories?
Cityscapes
What type of data is required by both We and Machine learning models to make accurate predictions?
Complete data
In the context of performance evaluation, what is the purpose of using Sanity Checks/Synthetic data?
To validate the correctness of algorithm outputs
Which benchmark dataset is commonly used for assessing machine learning models with numbers 0-9 distributed across 10 classes?
The MNIST database
What is a common issue faced in real-world datasets that impacts the accuracy of machine learning models?
Missing data
What is the main advantage of using pairwise deletion for handling missing data?
It is simple to implement and best used when the amount of missing data is minimal and MCAR
Which of the following is a potential drawback of using listwise deletion (complete case analysis) for handling missing data?
It can significantly reduce the dataset size
Which of the following is a key benefit of using multiple imputation methods, such as Multivariate Imputation by Chained Equations (MICE), for handling missing data?
They can reduce bias and increase the robustness of the model by incorporating uncertainty in the imputation process
What is the main assumption behind using mean/median/mode imputation for handling missing data?
The missing values can be effectively replaced by the mean/median/mode of the available data
Which of the following is a potential advantage of using regression imputation for handling missing data?
It can handle non-linear relationships between features
Which of the following is a key assumption behind using K-nearest neighbors (K-NN) imputation for handling missing data?
The missing values can be imputed based on the values of the $k$ nearest neighbors in the feature space
Which type of missing data occurs when the probability of a data point being missing is the same for all observations and is independent of both observed and unobserved data?
Missing Completely at Random (MCAR)
In a health survey, if younger people are less likely to report their age, the missingness of age data is considered:
Missing at Random (MAR)
If people with higher incomes are less likely to disclose their earnings, the missingness in income data is classified as:
Missing Not at Random (MNAR)
Which statement is true about Missing Not at Random (MNAR) data?
Its missingness is related to unobserved data.
If respondents randomly skip questions in a survey due to lack of attention, the missingness of data is considered:
Missing Completely at Random (MCAR)
What is the key challenge when dealing with missing data, as mentioned in the text?
Accurately estimating or handling the missing values to maintain model integrity and performance
Which statement about pairwise deletion is correct?
It is useful for covariance analyses using complete cases.
Which imputation technique is suitable for categorical data?
Mode imputation
Which imputation technique assumes a linear relationship between variables?
Regression imputation
Which imputation technique is effective for non-linear relationships and complex data structures?
K-Nearest Neighbors (K-NN) imputation
Which imputation technique is recommended when data is missing at random (MAR) or missing not at random (MNAR)?
Multiple imputation
Which statement about K-Nearest Neighbors (K-NN) imputation is correct?
It is computationally intensive and sensitive to outliers.
Test your knowledge on calculating average speed for a given distance and average resistance in parallel circuits. Explore concepts such as baselines, benchmarks, algorithms, dataset analysis, and various approaches in solving problems in math, analytics, and machine learning.
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