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.
Incorrect handling of missing data can lead to a(n) ________ in model performance.
- amplification
- boost
- degradation
- improvement
Incorrectly handling missing data can distort the data, thereby negatively affecting the model's ability to learn accurately from it and leading to a degradation in the model's performance.
The ________ correlation coefficient is based on the ranks of data rather than the actual values.
- Covariance
- Kendall's Tau
- Pearson's
- Spearman's
The Spearman's correlation coefficient is based on the ranks of data rather than the actual values. This makes it suitable for use with ordinal variables and resistant to outliers.
The choice of graph for data visualization largely depends on the __________ of the dataset.
- File format
- Shape
- Size
- Type of variables
The choice of graph for data visualization largely depends on the type of variables in the dataset. For example, categorical variables are best represented with bar charts or pie charts, while continuous variables might be better shown with histograms or box plots.
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.