Average Speed and Resistance in Circuits Quiz

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What is the impact of missing data on models according to the text?

Greater generalization of the model when data is missing.

No impact on model accuracy if the data is not randomly missing.

Reduced accuracy as the model is trained on an incomplete representation. (correct)

Increased accuracy due to a more focused representation of the problem space.

What is one potential reason for missing data related to technical challenges?

Systemic errors leading to selection bias.

Mistakes in data entry such as typos or omissions.

Individuals intentionally skipping questions in a survey.

Malfunctioning sensors due to sensitive topics like income or health. (correct)

How does missing data affect model training when it is not random?

Leads to biased models that misrepresent the underlying population or phenomena. (correct)

Results in increased generalizability of the models.

Ensures a more accurate representation of the problem space.

Enhances model training by introducing variability.

What could be a consequence of censoring in the context of missing data?

Creation of non-representative study samples. Signup and view all the answers

Which factor can contribute to missing data occurrence based on human factors?

Individuals choosing not to answer all questions in a survey. Signup and view all the answers

What is the purpose of using a Naive approach in problem-solving?

To simplify the problem by making unrealistic assumptions Signup and view all the answers

Which dataset contains images of 50 different cities with dense annotations grouped into 8 categories?

Cityscapes Signup and view all the answers

What type of data is required by both We and Machine learning models to make accurate predictions?

Complete data Signup and view all the answers

In the context of performance evaluation, what is the purpose of using Sanity Checks/Synthetic data?

To validate the correctness of algorithm outputs Signup and view all the answers

Which benchmark dataset is commonly used for assessing machine learning models with numbers 0-9 distributed across 10 classes?

The MNIST database Signup and view all the answers

What is a common issue faced in real-world datasets that impacts the accuracy of machine learning models?

Missing data Signup and view all the answers

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 Signup and view all the answers

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 Signup and view all the answers

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 Signup and view all the answers

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 Signup and view all the answers

Which of the following is a potential advantage of using regression imputation for handling missing data?

It can handle non-linear relationships between features Signup and view all the answers

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 Signup and view all the answers

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) Signup and view all the answers

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) Signup and view all the answers

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) Signup and view all the answers

Which statement is true about Missing Not at Random (MNAR) data?

Its missingness is related to unobserved data. Signup and view all the answers

If respondents randomly skip questions in a survey due to lack of attention, the missingness of data is considered:

Missing Completely at Random (MCAR) Signup and view all the answers

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 Signup and view all the answers

Which statement about pairwise deletion is correct?

It is useful for covariance analyses using complete cases. Signup and view all the answers

Which imputation technique is suitable for categorical data?

Mode imputation Signup and view all the answers

Which imputation technique assumes a linear relationship between variables?

Regression imputation Signup and view all the answers

Which imputation technique is effective for non-linear relationships and complex data structures?

K-Nearest Neighbors (K-NN) imputation Signup and view all the answers

Which imputation technique is recommended when data is missing at random (MAR) or missing not at random (MNAR)?

Multiple imputation Signup and view all the answers

Which statement about K-Nearest Neighbors (K-NN) imputation is correct?

It is computationally intensive and sensitive to outliers. Signup and view all the answers