You're working on a medical diagnosis problem where interpretability is crucial. How might you approach feature selection?
- By selecting random features
- By selecting the features that contribute most to the model's performance
- By using a black-box model
- By using all features
When interpretability is crucial, such as in a medical diagnosis problem, it's important to select the features that contribute most to the model's performance. This can make the model more understandable and transparent, which is important in fields where decisions have significant impacts and need to be explained.
What is the term used to describe the measure of the center of a distribution of data?
- Central Distribution
- Central Pattern
- Central Position
- Central Tendency
The term "Central Tendency" is used to describe the measure of the center of a distribution of data. Measures of central tendency aim to describe the central position of a distribution for a data set where the most typical values lie.
You are given a task to represent a complex data set in a visually appealing and easy to understand format for non-technical stakeholders. How would you approach this task?
- Avoid using colors to keep the graph simple
- Only use bar graphs, because everyone understands them
- Use complex graph types to show your technical expertise
- Use simple and familiar graph types, clear labels, and a thoughtful color scheme
When presenting complex data to non-technical stakeholders, it's best to use simple and familiar graph types, use clear labels and legends, and choose a thoughtful color scheme. Avoid using complex graphs that the audience might not understand, and remember that your goal is to make the data understandable, not to show off your technical skills.
The parameters of a Uniform Distribution are typically defined as _____ and _____, representing the minimum and maximum values respectively.
- a, b
- mean, standard deviation
- median, mode
- nan
The parameters of a Uniform Distribution are typically defined as 'a' and 'b', representing the minimum and maximum values respectively.
What method is typically used to handle missing categorical data by filling missing values?
- Listwise Deletion
- Mean Imputation
- Median Imputation
- Mode Imputation
'Mode Imputation' is a method that is typically used to handle missing categorical data. It fills missing values with the mode (most common value) of the available data. While it's a simple and fast method, it could introduce bias if the data is not missing completely at random.
______' in the EDA process typically involves cleaning the data and dealing with missing values and outliers.
- communicating
- concluding
- questioning
- wrangling
'Wrangling' in the EDA process typically involves cleaning the data and dealing with missing values and outliers. This step is crucial for preparing the data for subsequent exploration and analysis.
A team member has used a histogram to represent a dataset but the representation seems biased. What could be the potential issue?
- Improper choice of bin width
- Poor color choice
- The data was not cleaned properly
- The scale of the axes is wrong
One of the most common reasons a Histogram might appear biased is due to an improper choice of bin width. The bin width greatly affects the resulting shape and patterns. If the bins are too wide, important features may be hidden. If they are too narrow, the representation may appear too cluttered or noisy.
Given that you need to create a publication-quality figure, which Python library provides the best control over every aspect of the figure properties?
- Bokeh
- Matplotlib
- Plotly
- Seaborn
Matplotlib provides a low-level, object-oriented API for embedding plots into applications and gives the most control over every aspect of the figure properties. This makes it suitable for creating publication-quality figures.
When the data is skewed to the right, the _____ will usually be greater than the median.
- Mean
- Median
- Mode
- Range
When data is skewed to the right, it means there are a number of observations with large values, which pull the "Mean" up, making it greater than the median.
Which method of data imputation is generally most appropriate for MCAR data?
- Mean/Median imputation
- Prediction model
- Random Sample Imputation
- nan
For MCAR data, Random Sample Imputation is a good choice as it assumes that the data are missing completely at random. It works by taking random observations from the dataset and using these to replace the missing values.
What factors should be considered when assessing the aesthetics of a data visualization?
- The balance, simplicity, clarity, and color scheme
- The designer's personal taste
- The latest trends in data visualization
- The time it took to create the visualization
Aesthetics in data visualization involve multiple factors including balance (equal weightage to all parts), simplicity (avoiding unnecessary complexity), clarity (clearly understandable), and the color scheme (which can direct attention, represent categories, or express quantities). Good aesthetics make the data easy to understand and the message memorable.
What does the term "Multicollinearity" refer to in the context of Exploratory Data Analysis?
- A condition where the independent variables in a regression model are highly correlated
- A statistical method to determine the correlation between variables
- Correlation among three or more variables
- Correlation between two variables
Multicollinearity refers to a situation where two or more independent variables in a multiple regression model are highly correlated. If these variables are closely correlated, it can be hard for the model to determine the effect of each variable independently, which may lead to unstable estimates.