Domain 2: Exploratory Data Analysis

Question 1

Why should you address missing data?

Answer 1

Models may fail to train properly or could result in biased predictions if the missing data is not addressed.

Question 2

How do you identify missing data?

Answer 2

Various tools and function can check for missing values.

Question 3

What are three options to handle missing data?

Answer 3

Imputation: replace missing values w/ a statistic
Dropping: remove rows/columns with missing values
Predicting: use another ML model to predict/fill in missing values

Question 4

_____ refers to inaccurate, incomplete, or inconsistent data that can mislead or confuse your machine learning model.

Answer 4

Corrupt data

Question 5

Ways to handle corrupt data.

Answer 5

Validation rules, cleanup, and outlier detection

Question 6

_____ are commonly used words in a language that are often removed from text data before training text-based machine learning models. Examples include ‘the’, ‘is’, ‘at’, etc.

Answer 6

Stop words

Question 7

Why should you remove stop words?

Answer 7

Removing stop words helps in reducing the dimensionality of the text data and increases the model’s focus on words with more significant meaning.

Question 8

How do you remove stop words?

Answer 8

Identify them first, then filter them out

Question 9

_____ is about structuring data in a way that is suitable for the problem at hand and for the machine learning model to interpret.

Answer 9

Data formatting

Question 10

What kind of service can help transform and format data?

Answer 10

AWS Glue

Question 11

_____is the process of scaling individual samples to have unit norm. This process can help in the performance of machine learning models.

Answer 11

Normalization

Question 12

_____ is a strategy used to increase the diversity of data available for training models without actually collecting new data. This technique is particularly useful for tasks such as image and speech recognition, where input data can be modified slightly to create new training examples.

Answer 12

Data augmentation

Question 13

_____ is crucial when different features have different ranges. This ensures that the model treats each feature equally.

Answer 13

Feature scaling

Question 14

What are two common methods of scaling?

Answer 14

Min-Max Scaling (Normalization) - transforms by scaling each feature to a given range
Standardization (z-score normalization) - Centers the feature columns at mean 0 with standard deviation 1, so that the feature columns take the form of a normal distribution

Question 15

T/F: Sometimes, you may have to label some data using human experts prior to developing an ML model.

Answer 15

True - in a case where you don’t have sufficient labeled data

Question 16

What tool has an auto-labeling feature?

Answer 16

Mechanical Turk

Question 17

In natural language processing (NLP), text data requires conversion into _____format before being inputted to machine learning algorithms.

Answer 17

numerical

Question 18

What are some common techniques to convert text data into numerical format?

Answer 18

Bag of Words (BoW): Represents text by the frequency of each word.
Term Frequency-Inverse Document Frequency (TF-IDF): Considers the frequency of a term in relation to its frequency across multiple documents, reducing the influence of common terms.
Word Embeddings: Such as Word2Vec or GloVe, represent words in a high-dimensional space where the distance between words conveys semantic similarity.

Question 19

_____ data can be represented as a time-series of audio signals.

Answer 19

Speech

Question 20

Common techniques to extract features include:

Answer 20

Mel-Frequency Cepstral Coefficients (MFCCs): Captures the short-term power spectrum of sound.
Spectrogram: A visual way to represent the signal strength, or “loudness”, of a signal over time at various frequencies that are present in a waveform.

Question 21

_____ data requires features that can capture the visual information contained within.

Answer 21

Image

Question 22

Techniques for feature extraction from image data:

Answer 22

Color Histograms: Captures the distribution of colors in an image.
Edge Detection: Detects significant transition in color.
Convolutional Neural Networks (CNNs): Automatically discover the internal features from raw images.

Question 23

ImageNet: Used for training computer vision models.
LibriSpeech: An audio corpus for speech recognition research.
The Universal Dependencies: A collection of annotated text corpora in over 70 languages.

Answer 23

Popular public datasets

Question 24

When working with public datasets, feature extraction methods are typically determined by _____.

Answer 24

the nature of the data provided

Question 25

It involves choosing a subset of relevant features for model training.

Answer 25

Feature selection

Question 26

Techniques for feature selection

Answer 26

Filter Methods: Use statistical measures to score the relevance of features.
Wrapper Methods: Evaluate multiple models and select the best subset of features.
Embedded Methods: Perform feature selection as part of the model training process (e.g., regularization).

Question 27

_____ is integral to preparing data for machine learning, as it helps transform raw data into manageable groups of inputs.

Answer 27

feature extraction

Question 28

Involves transforming raw data into features that better represent the underlying problem to predictive models, leading to improved model accuracy on unseen data.

Answer 28

Feature engineering

Question 29

_____, also referred to as discretization, involves dividing continuous features into discrete bins or intervals, which can often lead to better performance for certain machine learning models that work better with categorical data.

Answer 29

Binning

Question 30

In the context of text processing, _____is the process of splitting text into individual terms or tokens. This is a critical step in the natural language processing (NLP) pipeline as it helps in preparing the text for embedding or feature extraction.

Answer 30

tokenization

Question 31

_____are data points that fall far away from the majority of the data. They can skew the results of data analysis and model training. Handling this is essential to prevent them from having an undue influence on the model’s performance.

Answer 31

Outliers

Question 32

Detection methods for outliers:

Answer 32

standard deviation from mean and interquartile range (iqr)

Question 33

_____ are new features that are created from one or more existing features, typically to provide additional context to a model, or to highlight relationships between features that may not be readily apparent.

Answer 33

Synthetic features

Question 34

_____ is a technique used to convert categorical variables into a form that could be provided to ML algorithms to do a better job in prediction. It creates a binary vector for each category of the feature.

Answer 34

One-hot encoding

Question 35

_____ involves techniques that reduce the number of input variables in a dataset. When high, these datasets can be problematic for machine learning models—a phenomenon often referred to as the “curse of ______.”

Answer 35

Dimensionality reduction, dimensionality

Question 36

Principal Component Analysis (PCA): A statistical procedure that converts a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables known as principal components.

t-Distributed Stochastic Neighbor Embedding (t-SNE): A nonlinear dimensionality reduction technique well-suited for embedding high-dimensional data into a space of two or three dimensions, which can then be visualized in a scatter plot.

Feature selection techniques: Methods such as backward elimination, forward selection, and recursive feature elimination help in selecting the most important features for the model.

Answer 36

Techniques for reducing dimensionality of data

Question 37

T/F: binning might improve a model’s performance for a continuous variable that has a nonlinear relationship with the target variable, but it may also result in the loss of information.

Answer 37

True

Question 38

T/F: Tokenization is essential for text analysis but not applicable for numerical data.

Answer 38

True

Question 39

T/F: Handling outliers is crucial, but one must decide whether to remove them or adjust them based on the context.

Answer 39

True

Question 40

T/F: Synthetic features can enhance model performance, but creating too many can lead to overfitting.

Answer 40

True

Question 41

T/F: One-hot encoding can introduce sparsity into the dataset, which might not be optimal for all models

Answer 41

True

Question 42

T/F: Dimensionality reduction techniques like PCA can remove noise and reduce overfitting but can make the interpretation of the model more challenging.

Answer 42

True

Question 43

_____ are used to display the relationship between two continuous variables. Each point on the graph represents the values of two variables for a particular observation.

Answer 43

Scatter plots

Question 44

_____ graphs are used to represent data points collected or recorded at many successive times, often with equal intervals.

Answer 44

Time series

Question 45

_____ are used to show the distribution of a dataset: how many times each value appears (i.e., frequency).

Answer 45

Histograms

Question 46

_____ (also known as box-and-whisker plots) are used to show the distribution of quantitative data and to highlight the median, quartiles, and outliers within the dataset.

Answer 46

Box plots

Question 47

_____can be used for data pre-processing, data exploration, and visualization with the same plotting libraries that we’ve mentioned here or through other visualization tools like Seaborn, which works on top of Matplotlib.

Answer 47

Jupyter notebooks

Question 48

_____summarize the main features of a data set in quantitative terms.

Answer 48

Descriptive statistics

Question 49

What kinds of measures might descriptive statististics include?

Answer 49

mean, median, and mode - depict center
std deviation and variance - indicate how spread out data is

Question 50

_____measures the strength and direction of the linear relationship between two variables.

Answer 50

Correlation

Question 51

A correlation coefficient ranges between_____.

Answer 51

-1 and 1

Question 52

A correlation coefficient close to 1 implies a _____ correlation (as one variable increases, the other tends to also increase)

Answer 52

strong positive

Question 53

A correlation coefficient close to -1 implies a _____correlation (as one variable increases, the other tends to decrease).

Answer 53

strong negative

Question 54

A correlation close to 0 suggests _______.

Answer 54

no linear relationship

Question 55

The _____ is used in the context of hypothesis testing to measure the strength of the evidence against the null hypothesis. It quantifies the probability of observing the given sample data, or something more extreme, assuming the null hypothesis is true.

Answer 55

p-value

Question 56

T/F: Summary statistics can also aid in feature engineering by providing insights into variable scales and distributions that can be normalized or standardized prior to modeling.

Answer 56

True

Question 57

_____ is a form of unsupervised learning that is used to find structure in a dataset.

Answer 57

Cluster analysis

Question 58

_____ is a method of cluster analysis that seeks to build a hierarchy of clusters. Observations are not assigned to clusters definitively but instead are linked to nearby clusters with the data ultimately represented as a tree.

Answer 58

Hierarchical clustering

Question 59

Collecting and preparing the data.
Computing a distance matrix to assess the similarity between data points.
Constructing a dendrogram to represent the distance or dissimilarity between clusters.
Deciding on a threshold for cutting the dendrogram to define the number of clusters.

Answer 59

Example steps in hierarchical clustering

Question 60

____ involves evaluating the results of your clustering to ensure they are sensible and effectively capture the natural groupings within the data.

Answer 60

Diagnosis

Question 61

Diagnostic methods might include _____ and _____, using measures such as ____.

Answer 61

evaluating intra-cluster homogeneity, inter-cluster separation, silhouette scores

Question 62

The ____ is a heuristic used in determining the number of clusters in a dataset.

Answer 62

elbow method

Question 63

The idea is to run the clustering for a range of cluster values (k) and calculate the sum of squared distances from each point to its assigned center.

Answer 63

elbow method

Question 64

When plotted, the sum of squares will decrease as k increases, but the rate of decrease will sharply change at some point, creating an _____ in the graph.

Answer 64

elbow

Question 65

Why is it important to choose the size of clusters in cluster analysis?

Answer 65

Small clusters might be too specific and might not generalize well, while overly large clusters may be too inclusive, failing to provide useful differentiation.

Question 66

Define a range of possible cluster sizes.
Use a metric (e.g., silhouette score) to quantify the performance for each size.
Opt for the size that maximizes performance according to the chosen metric.

Answer 66

Steps to adjust cluster size efficiently

Question 67

_____applies to columns in your dataset, which are numerical.

Answer 67

Normalization

Question 68

T/F: As a best practice, remove any duplicate data/rows you may have in your dataset.

Answer 68

True

Question 69

You center all the values around the mean value for that column with unit standard deviation, which is useful when your data has a normal distribution or a close to normal distribution.

Answer 69

Standardization

Question 70

If your data distribution is heavily skewed and you have a large number of outliers, consider using a_____to first transform your data to a distribution that looks similar to a normal distribution and then use standardization.

Answer 70

log transformation

Question 71

_____ refers to simply labeling the unique values in a categorical column with integers.

Answer 71

Label encoding

Question 72

In _____, you can convert each value to its own column and assign a 1 or 0 depending on whether that row has that value.

Answer 72

One-hot encoding