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Fundamentals of Data Science > Chapter 3- Data Exploration > Flashcards

Chapter 3- Data Exploration Flashcards

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1
Q

What is in a data quality report?

A

Tabular reports that describe the characteristics of each feature in an ABT using standard measures of central tendency and variation

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2
Q

Which data visualizations accompany tabular reports?

A
  • Histogram for continuous features we can apply Quantitative scales to
  • Bar plot for each categorical feature we can apply Qualitative scales to
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3
Q

What are frequency histograms?

A

The consist of a set of rectangles/ bars that reflect the counts of frequencies of the classes present in the given data

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4
Q

How do you get to know categorical features?

A
  • Examine the mode, 2nd mode, mode % and 2nd mode %
  • These tell us the most common levels within the features and will identify if any levels dominate the dataset
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5
Q

How do you get to know continuous features?

A
  • Examine the mean and standard deviation
  • they will help us get a sense of the central tendency and variation of the values within the dataset for the feature
  • Examine the minimum and maximum (and other quartiles)
  • They will help us understand the range that is possible for each feature
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6
Q

What are the types of histogram characteristics?

A
  • Uniform
  • Normal (unimodal)
  • Unimodal (skewed right)
  • Unimodal (skewed left)
  • Exponential
  • Multimodal
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7
Q

What is a uniform distribution

A
  • It indicated that a feature is equally likely to take a value in any of the ranges present
  • Sometimes it shows that a descriptive feature contains an ID rather than a measure of something more interesting
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8
Q

What is Normal (unimodal) distribution?

A
  • They have a strong tendency toward a central value and symmetrical variation on either side of the central tendency
  • It is called unimodal because of the single peak around the central tendency
  • Naturally occurring phenomena follow normal distribution
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9
Q

What is the skew when the data contains some very high values?

A

Skew right/ positive skew

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10
Q

What is the skew when the data contains some very low values?

A

Skew left/ Negative skew

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11
Q

What is the mode and median relationship during skews?

A
  • Right skewed- Mode < Median
  • Left skewed- Mode > Median
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12
Q

What is exponential distribution?

A
  • The likelihood of certain values occurring is very high but diminishes rapidly for higher or lower values
  • It is a clear warning sign that outliers are likely
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12
Q

What is multimodal distribution?

A
  • It has two or more very commonly occurring ranges of values that are clearly separated
  • Bi-modal distribution can be thought of as two normal distributions pushed together
  • Occurs when a feature contains a measurement made across a number of distinct groups
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12
Q

What are the two things multimodal distribution is a cause for?

A
  • Caution because measures of central tendency and variation tend to break down for multimodal data
  • Optimism because if we are lucky, the separate peaks in the distribution will be associated with the different target levels we are trying to predict
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13
Q

What happens when a distribution has different means but identical standard deviations?

A

The distribution moves side to side

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14
Q

What happens when a distribution has identical means but different standard deviations?

A

Distribution moves up and down

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15
Q

What does the 68-95-99.7 rule state?

A
  • 68% of observations will be between one standard deviation of mean (mean - sd)
  • 95% of observations will be within two standard deviations of mean (mean - 2sd)
  • 99.7% of observations will be within three standard deviations of mean (mean - 3sd)
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16
Q

What is a data quality issue?

A

It is loosely defined as anything unusual about the data in an ABT

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17
Q

What are the most common data quality issues?

A
  • Missing values
  • Irregular cardinality
  • Outliers
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18
Q

What are the data quality issues we identify from a data quality report?

A
  • Issues due to invalid data (syntax error)
  • Issues due to valid data (human error)
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19
Q

How do you handle data issues?

A
  • Approach 1: Drop any features that have missing values
  • Approach 2: Apply complete case analysis (delete records)
  • Approach 3: Derive a missing indicator feature from features with missing value
  • Approach 4: Impute the missing values
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20
Q

What are the effects of approach 1?

A
  • It can result in massive, and frequently needless loss of data
  • Only features with 60% excess missing values should be considered for removal
  • An alternative is to derive a missing indicator feature for them, could be categorical
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21
Q

What is approach 2?

A
  • We delete instances that are missing one or more feature values
  • This results in significant amounts of data loss and can introduce a bias to the dataset
  • This should rarely be used and only when a data instance is missing values for multiple features
  • It is recommended to remove instances that are missing the value in the target feature
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22
Q

Explain approach 3

A
  • This could be a categorical feature that flags the missing data as a new label (unknown in marital status)
  • Or a binary feature that flags whether the value was present or missing (T/F)
  • When missing indicator features are used the original feature is usually discarded
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23
Q

Explain approach 4

A
  • Imputation replaces missing feature values with a plausible estimated value based on the feature values that are present
  • Most commonly you replace missing values with a measure of central tendency of that feature
  • We should be reluctant to use imputation on features missing in excess of 30% of their values
  • Strongly recommend against using imputation on features missing in excess of 50% of their values
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24
Q

What is the easiest way to handle outliers?

A

Use clamp transformation

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25
Q

What does clamp transformation do?

A

It clamps all values above an upper threshold and below a lower threshold to these threshold values thus removing the offending outliers

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26
Q

How do you identify outliers?

A

Three popular rules of thumb:
- Values located at least 1.5*IQR above Q3 or below Q1. Requires sorting so is expensive
- Values more than 2 standard deviations from the mean. Both mean and sd can usually be computed easily
- 2% from top and bottom of your ordered data. Trivial to implement but hard to scientifically defend heuristics

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27
Q

What is a scatter plot?

A
  • It is based on 2 axes, horizontal and vertical axis
  • Each instance is represented by a point on the plot determined by the values for that instance of the two features involved
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28
Q

What is a scatter plot matrix?

A
  • Scatter plot matrix (SPLOM) shows scatter plots for a whole collection of features arranged into a matrix
  • It is useful for exploring the relationship between groups of features
  • It is a visualization of the correlation matrix
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29
Q

What else can we do in addition to visually inspecting scatter plots?

A

Calculate formal measures of the relationship between two continuous features using covariance and correlation

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30
Q

What range does covariance usually fall into?

A
  • [Negative infinity, Positive infinity]
  • Negative values indicate a negative relationship
  • Positive values indicate a positive relationship
  • Values near zero indicate that there is little to no relationship
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31
Q

What is correlation?

A
  • A normalized form of correlations
  • Range [-1, +1]
  • Correlation values close to -1 indicate strong negative correlation
  • Values close to 1 indicate a strong positive correlation
  • Values around 0 indicate no correlation
  • Features that have no correlation are said to be linearly independent
32
Q

What tools are useful for exploring relationships between multiple continuous features?

A
  • Covariance matrix
  • Correlation matrix (normalized version of covariance matrix)
33
Q

What are the three types of measures for correlation tests?

A
  • Distributive
  • Algebraic
  • Holistic (non-algebraic)
34
Q

Explain the distributive measure

A
  • If results are derived by applying the function to n aggregate values is the same as the one derived by applying the function on all the data without partitioning
  • count(), sum(), min(), max()
35
Q

Explain algebraic measure

A
  • If the measure can be computed by an algebraic function with M arguments (where M is a bounded integer), each of which is obtained by applying a distributive aggregate function
  • Average(), StandardDeviation(), Top-K()-K largest values, CenterOfMass()
36
Q

Explain holistic (non-algebraic measure)

A
  • If there is no constant bound on the storage size needed to describe a sub aggregate
  • Median(), Rank()
  • Sometimes we may be able to approximate Holistic Measures using Non-Holistic ones
37
Q

How does correlation help with measuring relationships between features?

A
  • Correlation is a good measure of the linear relationship/ dependency between two continuous features, but it is by no means perfect
  • When we focus only on correlation values without data visualization, it may cause us to not notice other dependencies between our features
  • Correlation does not necessarily imply causation
38
Q

What are the reasons for mistakenly assumed causation?

A
  • Mistaking the order of a causal relationship
  • Like when you assume playing basketball makes people tall when people actually choose to play basketball because of their height
  • Unawareness of the existence of a third (confounding) feature
  • There is a third feature that influences the two others making them appear highly correlated
  • Like assuming the drinking coffee causes cardiovascular disease when actually smoking causes it and most smokers are coffee drinkers
39
Q

What is the simplest way to visualize the relationship between two categorical variables?

A

Using a collection of small multiple bar plots

40
Q

What situation can you use stacked bar plots as an alternative to the small multiples approach?

A

If the number of levels of one of the features being compared is no more than three

41
Q

What do you use to visualize the relationship between a continuous feature and categorical feature?

A

A small multiples approach that draws a histogram of the values of the continuous feature for each level of the categorical feature

42
Q

What other way can you visualize the relationship between categorical and continuous features?

A

Using a box plot

43
Q

What does a boxplot do?

A
  • It lets us visually show the five-number summary of a distribution
  • Trimmed minimum, Q1, Median, Q3, Trimmed maximum
44
Q

What is a violin plot?

A
  • A combination of a box plot and density plot
  • It provides even more detailed information about our data distribution
45
Q

What do data preparation techniques do?

A

They change the way data is represented just to make it more compatible with certain machine learning algorithms

46
Q

What are the data preparation techniques?

A
  • Normalization (feature scaling)
  • Binning
  • Sampling
47
Q

Explain normalization

A
  • These techniques can be used to change a continuous feature to fall within a specified range while maintaining the relative differences between the values for the feature
48
Q

Why must you remember to scale back when presenting your findings?

A

To ensure you retain all information about the normalization procedure

49
Q

Why is normalization done?

A
  • To assure no feature with larger range is misleading out ML algorithm
  • To safely fit the original data into a range desired by out computer/ ML software
50
Q

What are the common types of data normalization?

A
  • Min-Max normalization (Range normalization)
  • Z-score normalization (Standardization)
  • Decimal scaling
51
Q

Explain Min-Max normalization

A
  • It is used to linearly convert all feature values into the new range
  • It allows us to maintain all relative differences between the values for the feature
52
Q

What is the benefit of min-max normalization?

A
  • It is convenient and intuitive
  • It lets us fit the data into any range we want easily while the original proportions between data points are guaranteed to be perfectly preserved
53
Q

What is the drawback of range normalization?

A
  • It is sensitive to the presence of outliers in a dataset
  • It carries the risk of easy generation of out-of-bound errors for new entries
54
Q

Explain Z-score normalization

A
  • It is the process of transforming data to standard scores
  • A standard score measures how many standard deviations a feature value is from the mean for that feature
55
Q

How does z-score normalization work?

A
  • It squashes the values of the feature so that the feature values have a mean of 0 and standard deviation of 1
  • This results in the majority (68%) of the feature values being in a range of [-1, +1]
  • If the data in all features follow a Gaussian/Normal distribution they will be converted to standard normal and comparisons between different features become easier.
56
Q

Compare Min-Max and Z-score normalization

A
  • It is easy to specify the output range for Min-Max Normalization (often [0,1] or [0.1, 0.9] or [1, 10 to remain positive)
  • It is more of a challenge to guarantee the exact range from Z-score normalization
  • Standardization is slightly more resistant than Min-Max to out-of-bound errors
  • Standardization will introduce distortions to the data if it is not normally distributed
57
Q

What is robust scaling?

A
  • A scaling method impacted less by outliers
  • It is good because outliers impact sample mean and standard deviations very significantly
  • It removes the median and scales using IQR
58
Q

Explain Normalization by Decimal Scaling

A
  • It transforms data by removing the decimal points of the feature values
  • The number of decimal points moved depends on the maximum absolute value of that feature
  • It is trivial to implement
  • It is highly out-of-bounds resistant
59
Q

When is Decimal Scaling used?

A
  • Signal community when we want to separate noise from data easily
  • Recommended for dealing with Skewed left unimodal or Exponential data distributions
60
Q

Explain binning

A

This involves converting a continuous feature into an ordinal feature

61
Q

How do you perform binning?

A

Define a series of ranges called bins for the continuous feature that corresponds to the levels of the new categorical feature we are creating

62
Q

What are the popular way of defining bins

A
  • Equal-width binning
  • Equal-frequency binning
63
Q

What are the trade-off for deciding on the number of bins?

A
  • If we set the number of bins too low we may lose a lot of information
  • If the number of bins is too high we may have very few instances in each bin or end up with empty bins
  • That is preferable to losing information
64
Q

Explain equal-width binning

A
  • It splits the range of the feature values into b bins each of the size range/b
  • We want to store the bucket boundaries and the sum of frequencies of the values with each bucket
65
Q

Explain the construction of equal-width binning

A
  • Use one pass over ABT to construct an accurate equal-width histogram
  • Keep a running count for each bucket
  • If scanning is not acceptable use sampling
  • Construct a histogram on ABT sample and scale the frequencies by |ABT|/|ABT sample|
66
Q

Explain the maintenance of equal-width binning

A
  • Incremental maintenance: for each update in ABT increment/decrement the corresponding bucket frequencies
  • Periodical re-computation: because distribution changes slowly
67
Q

Explain equal-frequency binning

A
  • It first sorts the feature values in ascending order and then places an equal number of instances into each bin starting with bin 1
  • The number of instances in each bin is (total number of instances)/ (number of bins)
68
Q

Explain the construction of equal-frequency binning

A
  • Sort all data entries then take equally spaces slices
  • Sampling also works
69
Q

Explain the maintenance of equal-frequency binning

A
  • Incremental maintenance
    • Merge adjacent buckets with small counts
    • Split any bucket with large count
      • Select the median value to split
      • Need a sample of the values within this bucket to work well
  • Periodic re-computation also works
70
Q

Explain sampling

A
  • It is used when the dataset is so large we do not use all the data available to us in an ABT
  • We have to be careful to ensure that the resulting datasets are still representative of the original dataset and that no unintended bias is introduced during this process
71
Q

What are the common forms of sampling?

A
  • Top sampling
  • Random sampling
  • Stratified sampling
  • Under-sampling
  • Over-sampling
72
Q

Explain top sampling

A
  • Selects the top percentage of instances from the dataset to create a sample
  • Runs the risk of introducing bias since the sampling will be affected by any ordering of the original dataset
  • Recommended to avoid
73
Q

Explain random sampling

A
  • Randomly selects a proportion of percentages of the instances from the large dataset to create a smaller set
  • It is a good choice in most cases since the randomness should avoid introducing bias
74
Q

Explain stratified sampling

A
  • It ensure that the relative frequencies of the levels of a specific feature are maintained in the sampled dataset
75
Q

How do you perform stratified sampling?

A
  • Divide the instances in the dataset into groups
  • Each group contains only instances that have a particular level for the stratification feature
  • Percentages of the instances in each stratum are randomly selected
  • These selections are combined to give an overall sample of percentages of the original dataset
76
Q

When is over-sampling and under-sampling used?

A

When we would like a sample to contain different relative frequencies of the levels of a particular feature to the distribution in the original dataset

77
Q

Explain under-sampling

A
  • Begins by dividing a dataset into groups where each group contains only instances that have a particular level for the feature to be under-sampled
  • The number of instances in the smallest group is the under-sampling target size
  • Each group that has more instances than the smallest is randomly sampled by the appropriate percentage to create a subset that is the under-sampling target size
  • The under-sampled groups are combined to create the overall under-sampled dataset
78
Q

Explain over-sampling

A
  • It addresses the same issue as under-sampling but the opposite way
  • After dividing the dataset into groups the number of instances in the largest group becomes the over-sampling target size
  • From each smaller group, we create a sample containing that number of instances using random sampling with replacement
  • Larger samples are combined to form the overall over-sampled dataset

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