Data Validation and Classification Quiz

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What is the purpose of identifying outliers in a dataset?

To improve the accuracy of statistical methods (correct)

To introduce errors in the data

To decrease the stability of neural networks

To maintain inconsistencies in the data

In the context of data analysis, what is the role of normalization?

To introduce errors into the data

To scale and transform data for better analysis (correct)

To maintain extreme values in the dataset

To prevent the identification of outliers

Which statistical methods benefit from normalized data according to the text?

Neural Networks and k-Means (correct)

Methods that rely on outliers

Methods that are insensitive to data distribution

Methods that avoid data preprocessing

What is the significance of maintaining consistency in class labels for data from different origins?

To avoid errors in data entry Signup and view all the answers

Why might a value like 192.5 pounds be considered an outlier in a dataset focused on whole-numbered weight values?

It represents an error in data labeling Signup and view all the answers

How does a histogram aid in identifying outliers in a dataset?

By highlighting extreme values in the dataset Signup and view all the answers

What is the downside of deleting records containing missing values?

Creates a biased subset Signup and view all the answers

Which method of handling missing data involves replacing missing numeric values with 0.0 and missing categorical values with 'Missing'?

Replacing with User-defined Constant Signup and view all the answers

Why is replacing missing values with random values considered superior to mean substitution?

Measures of location and spread are closer to the original Signup and view all the answers

When replacing missing values with random values, what is the potential risk regarding the resulting records?

They might introduce outliers Signup and view all the answers

In handling missing data, why is it important to consult domain experts regarding the replacement approach?

To evaluate benefits and drawbacks Signup and view all the answers

Which method involves replacing missing values based on the mode for categorical fields and the mean for numeric fields?

Replacing with Mode or Mean Signup and view all the answers

What is a common characteristic of the two possible outliers identified in the scatter plot of mpg against weightlbs?

They both have extremely high gas mileage. Signup and view all the answers

What is a common measure of center used for datasets with skewed distributions?

Median Signup and view all the answers

What is a measure of spread that includes the range, standard deviation, mean absolute deviation, and interquartile range?

Standard Deviation Signup and view all the answers

In data transformation, why is it important to normalize numeric field values?

To ensure all variables have the same range of effect on results Signup and view all the answers

Which normalization technique involves scaling the field value based on the range between the minimum and maximum values?

Min-max normalization Signup and view all the answers

In transformation to achieve normality, what analysis tool is used to check if the distribution is normal?

Normal probability plot Signup and view all the answers

Why is it suggested that ID fields should be filtered out from downstream data mining algorithms?

ID fields do not add value to the analysis. Signup and view all the answers

What is a common issue with variables containing a high percentage of missing values?

They may bias imputation strategies. Signup and view all the answers

'Double-counting' can occur when including what type of variables in analysis?

'Correlated' variables Signup and view all the answers