How does improper handling of missing data impact the precision-recall trade-off in a model?
- Degrades both precision and recall.
- Degrades precision but improves recall.
- Improves both precision and recall.
- Improves precision but degrades recall.
Incorrectly handling missing data can lead to incorrect learning and misclassification, degrading both the precision (incorrectly identified positives) and recall (missed true positives) of the model.
Why is the standard deviation a useful measure of dispersion?
- It is the same as variance
- It's a measure of average dispersion
- It's the most complex measure of dispersion
- It's unaffected by outliers
The "Standard Deviation" is a useful measure of dispersion because it is a "Measure of average dispersion". It tells us how much, on average, each value in the data set deviates from the mean.
Suppose you are visualizing survey data where the responses are highly skewed towards one particular option. How can you accurately depict this bias in your visualization?
- Use a pie chart with equal slices for each response
- Use a bar graph with the y-axis starting at the lowest response value
- Use a bar graph with the y-axis starting at zero
- Present the data in a table, because graphs can't show this
If the responses to a survey question are highly skewed towards one option, a bar graph with the y-axis starting at zero can accurately depict this bias. This type of graph clearly shows the difference in the number of responses for each option, allowing viewers to see the skewness.
What are the key steps involved in an EDA process?
- Clean, Transform, Visualize, Model
- Gather, Analyze, Report
- Plan, Perform, Evaluate
- Question, Wrangle, Explore, Conclude, Communicate
The key steps in EDA are: Question (identifying the questions you want to answer), Wrangle (collecting the necessary data and cleaning/preprocessing it), Explore (investigating the data, looking for patterns and relationships, often through visualizations), Conclude (interpreting the analysis, answering the questions), and Communicate (presenting your findings effectively to others). This iterative process can offer a robust approach to understanding the data's features and underlying structures.
In a scenario where you need to produce a quick-and-dirty plot with minimal coding, which Python library would be the most appropriate?
- Bokeh
- Matplotlib
- Plotly
- Seaborn
Seaborn is a high-level interface based on Matplotlib that provides a high-level interface for drawing attractive statistical graphics with fewer lines of code. This makes it more suitable for quickly producing plots with minimal coding.
What are the key statistical tools used in Confirmatory Data Analysis (CDA)?
- Box-Plot, Scatter Plot, Histogram, and Density Plots
- Hypothesis Testing, Regression Analysis, Chi-Squared Test, and ANOVA
- PCA, LDA, t-SNE, and UMAP
- Random Forests, SVM, Neural Networks, and Gradient Boosting
In CDA, the primary goal is to confirm or refute the hypotheses that were generated during EDA. Key statistical tools used in CDA include Hypothesis Testing, Regression Analysis, Chi-Squared Test, and Analysis of Variance (ANOVA).
When would a scatter plot be less effective in identifying outliers?
- When the data has no correlation
- When the data is normally distributed
- When the data points are closely grouped
- When there are many data points
A scatter plot may be less effective in identifying outliers when the data points are closely grouped because it would be hard to visually identify points that are far away from the others.
The _____ Distribution is used for modeling the number of times an event occurs in an interval of time or space.
- Binomial
- Normal
- Poisson
- Uniform
The Poisson Distribution is used for modeling the number of times an event occurs in an interval of time or space.
How does 'binning' help in dealing with outliers in a dataset?
- By dividing the data into intervals and replacing outlier values
- By eliminating irrelevant variables
- By identifying and removing outliers
- By normalizing the data
Binning helps in dealing with outliers by dividing the data into intervals or 'bins' and replacing outlier values with summary statistics like the bin mean or median.
Suppose you have a data set with many missing values and outliers. In which step of the EDA process would you primarily deal with these issues?
- In the communicating phase
- In the exploring phase
- In the questioning phase
- In the wrangling phase
During the 'wrangling' phase of the EDA process, data analysts deal with data cleaning tasks which includes handling missing values and dealing with outliers. Data wrangling involves transforming and cleaning data to enable further exploration and analysis.