_____ data provides numerical measurements and it can be broken down into two subcategories: continuous and discrete.
- Nominal
- Ordinal
- Qualitative
- Quantitative
Quantitative data provides numerical measurements and it can be divided into two types: continuous (data that can take any value within a range) and discrete (data that can only take certain values).
Given a machine learning algorithm that is highly sensitive to the range of input values, which scaling technique should you implement?
- Min-Max scaling because it scales all values between 0 and 1
- No scaling, as the original data values should be maintained
- Robust scaling because it is not affected by outliers
- Z-score standardization because it creates a normal distribution
Min-Max scaling is suitable when the algorithm is sensitive to the range of input values, as it scales all feature values into a specified range (usually 0-1). This ensures that all features have the same scale.
How is the whisker of a box plot usually calculated?
- Mean ± Standard Deviation
- Median ± Interquartile Range
- Minimum and maximum values of the dataset
- Q1 - 1.5 * IQR, Q3 + 1.5 * IQR
The whisker of a box plot is typically calculated using the formula: Q1 - 1.5 * IQR and Q3 + 1.5 * IQR.
What is the key characteristic of a Uniform Distribution?
- All values are equally likely
- Most values are around the mean
- Values are skewed to the left
- Values are skewed to the right
In a Uniform Distribution, all values have the same frequency/probability. That is, they are all equally likely.
Your EDA reveals a non-normal distribution of data in your dataset. How might this insight affect your choice of machine learning models or algorithms?
- You should always normalize your data
- You should use only non-parametric models
- You should use only unsupervised learning models
- Your choice of ML models might be influenced, as some models make certain assumptions about the data distribution
The distribution of data can influence the choice of machine learning models or algorithms. Some models, such as linear and logistic regression, make certain assumptions about the data distribution (i.e., they expect the input or output to be normally distributed). If these assumptions are violated, the model may perform poorly. Therefore, understanding the data distribution can guide you in choosing the most appropriate models or in deciding whether to transform your data.
How does multiple imputation handle missing data?
- It deletes rows with missing data
- It estimates multiple values for each missing value
- It fills missing data with mode values
- It replaces missing data with a single value
Multiple imputation estimates multiple values for each missing value, instead of filling in a single value for each missing point. It reflects the uncertainty around the true value and provides more realistic estimates.
Mishandling missing data can lead to a high level of ________, impacting model performance.
- bias
- precision
- recall
- variance
If missing data is handled improperly, it can lead to biased training data, which can cause the model to learn incorrect or irrelevant patterns and, as a result, adversely affect its performance.
Your data shows a notable difference between the mean and the median values. Which type of scaling would be least affected by this discrepancy?
- All scaling methods are affected by this discrepancy
- Min-Max scaling because it scales all values between 0 and 1
- Robust scaling because it uses median and quartile ranges
- Z-score standardization because it creates a normal distribution
Robust scaling uses the median and interquartile range to scale the data, so it is not affected by the mean and is thus least affected by a discrepancy between the mean and the median.
How does platykurtic kurtosis shape the data distribution?
- It results in a distribution with heavier tails and a flatter peak.
- It results in a distribution with lighter tails and a flatter peak.
- It results in a distribution with lighter tails and a higher peak.
- It results in a perfectly symmetrical distribution.
Platykurtic kurtosis results in a data distribution that has lighter tails and a flatter peak compared to a normal distribution. This indicates a lower frequency of extreme values or outliers.
How does the application of Predictive Modeling differ from EDA and CDA in data-driven decision making?
- Predictive Modeling does not play a role in data-driven decision making.
- Predictive Modeling is used after EDA and CDA to make future predictions based on the data.
- Predictive Modeling is used before EDA and CDA to anticipate the outcomes.
- Predictive Modeling, EDA, and CDA all serve the same purpose.
Predictive Modeling, which is often performed after EDA and CDA, is used to make future predictions based on the data. These predictions can inform decision-making processes, particularly in data-driven organizations.
Which type of correlation is based on ranks and perfect for ordinal data?
- Kendall's Tau
- Pearson's correlation
- Point-Biserial Correlation
- Spearman's correlation
Spearman's correlation, also known as Spearman's rank correlation, is based on ranks and is perfect for ordinal data. It assesses how well the relationship between two variables can be described using a monotonic function. It is less sensitive to outliers and non-linear relationships compared to Pearson's correlation.
In the context of a Binomial Distribution, a "success" is defined as _____.
- a positive outcome
- a random event
- an outcome of interest
- an outcome that occurs most frequently
In the context of a Binomial Distribution, a "success" is defined as an outcome of interest, which could be positive, negative, or neutral depending on the context.