Improper handling of missing data can affect the ________ of a model, thereby impacting its ability to generalize on unseen data.
- bias-variance tradeoff
- overfitting
- regularization
- underfitting
Improper handling of missing data can adversely affect the bias-variance tradeoff of a model. This can lead to issues such as overfitting or underfitting, which impact the model's ability to generalize to unseen data.
In which situations would you prefer a heatmap over a scatter plot?
- When dealing with a single variable
- When the data is non-numeric
- When visualizing a time series
- When visualizing the correlation between multiple variables
You would prefer a heatmap over a scatter plot when visualizing the correlation between multiple variables. A heatmap can visualize any number of variables at once and is particularly effective when the dataset contains many variables.
Which of the following types of analysis provides the least assumptions about data: EDA, CDA, or Predictive Modeling?
- CDA
- EDA
- Predictive Modeling
- They all make the same number of assumptions.
EDA makes the least assumptions about data. While CDA and Predictive Modeling typically require some assumptions about the data's distribution or the relationships between variables, EDA is a more open-ended exploration of the data's structure and patterns.
You have a scatter plot with a strong positive correlation, but a few points are far from the correlation line. What might these points represent?
- Correlated data points
- False positives
- Normal data points
- Outliers
In a scatter plot, points that are far away from the correlation line often represent outliers.
You create a histogram of a dataset and notice that the frequency count is very high on the far right of the distribution but drops significantly after that. What can be inferred from this?
- Data has a negative skewness
- Data has a positive skewness
- Data is evenly distributed
- Data is normally distributed
If the frequency count in a histogram is very high on the far right but drops significantly after that, it can indicate that the data has a positive skewness.
A data analyst needs to demonstrate the occurrence of outliers in a dataset using a plot. Which plot type would you recommend and why?
- Bar graph
- Box plot
- Line graph
- Scatter plot
The Box plot is ideal for demonstrating outliers in a dataset. The 'whiskers' in a box plot represent the range for the bulk of the data, and any data point that falls outside of this range is visually represented as an outlier.
The process of combining highly correlated variables into one is called _________.
- Data Aggregation
- Principal Component Analysis (PCA)
- Standardization
- Variance Inflation
When dealing with multicollinearity, one approach is to combine the correlated variables into one using a technique such as Principal Component Analysis (PCA). PCA creates new uncorrelated variables that capture the information of the original variables.
The ______ of a scatter plot may indicate the presence of outliers in the dataset.
- correlation
- scatter
- slope
- trend line
In a scatter plot, the scattering or spread of data points can help identify outliers. Points that are distant from the main concentration of data can indicate potential outliers.
In what scenario would you choose standardization over Min-Max scaling?
- When the algorithm requires features to be on the same scale and the data is normally distributed
- When the maximum and minimum values are unknown
- When there are no outliers in the data
- When you need to normalize the distribution
You would choose standardization over Min-Max scaling when the algorithm requires features to be on the same scale and the data is normally distributed. Standardization does not bound values to a specific range like Min-Max scaling, which can be useful for algorithms that do not require input features to be within a certain range.
The diagonals of a pairplot often show the _____ of the individual variables.
- frequency distribution
- mean
- mode
- standard deviation
In pairplot, the diagonals often show the frequency distribution of the individual variables. This provides an understanding of the distribution of individual variables in addition to their relationships with other variables.