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.
How can one interpret the colors in a heatmap?
- Colors have no significance in a heatmap
- Colors represent different categories of data
- Colors represent the magnitude of the data
- Darker colors always mean higher values
In a heatmap, colors represent the magnitude of the data. Usually, a color scale is provided for reference, where darker colors often correspond to higher values and lighter colors to lower values. However, the color scheme can vary.
In what situations is it more appropriate to use the interquartile range instead of the standard deviation to measure dispersion?
- When the data has no outliers
- When the data is normally distributed
- When the data is perfectly symmetrical
- When the data is skewed or has outliers
The Interquartile Range (IQR) is a more appropriate measure of dispersion when the data is "Skewed or has outliers" as it is not affected by extreme values.
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.
How can regularization techniques contribute to feature selection?
- By adding a penalty term to the loss function
- By avoiding overfitting
- By reducing model complexity
- By shrinking coefficients towards zero
Regularization techniques contribute to feature selection by shrinking the coefficients of less important features towards zero. This has the effect of effectively removing these features from the model, thus achieving feature selection.
What type of data visualization method is typically color-coded to represent different values?
- Heatmap
- Histogram
- Line plot
- Scatter plot
Heatmaps are typically color-coded to represent different values. In a heatmap, data values are represented as colors, making it an excellent tool for visualizing large amounts of data and the correlation between different variables.
What is the potential disadvantage of using listwise deletion for handling missing data?
- It causes overfitting
- It discards valuable data
- It introduces random noise
- It leads to multicollinearity
The potential disadvantage of using listwise deletion for handling missing data is that it can discard valuable data. If the missing values are not completely random, discarding the entire observation might lead to biased or incorrect results because it might exclude certain types of observations.
If a data point's Z-score is 0, it indicates that the data point is _______.
- above the mean
- an outlier
- below the mean
- on the mean
A Z-score of 0 indicates that the data point is on the mean.