Question 1

Which of the following statements a), b) or c) is false?
A) Supervised machine learning falls into two categories-xe "classification (machine learning)"classification and xe "regression"regression.
B) You train machine-learning models on datasets that consist of rows and columns. Each row represents a data feature. Each column represents a sample of that feature.
C) In supervised machine learning, each sample has an associated label called a target (like "spam" or "not spam" for classifying e-mails). This is the value you're trying to predict for new data that you present to your models.
D) All of the above statements are true.

Accepted Answer

Explanation: The correct description for datasets in machine learning is that each row represents a sample or instance, and each column represents a feature of that sample. Statement B incorrectly swaps these definitions.

Question 2

Which of the following statements a), b) or c) is false?
A) The amount of data that's available today is already enormous and continues to grow exponentially-the data produced in the world in the last few years alone equals the amount produced up to that point since the dawn of civilization.
B) People used to say "I'm drowning in data and I don't know what to do with it. With machine learning, we now say, "Flood me with big data so I can use machine-learning technology to extract insights and make predictions from it."
C) The big data phenomenon is occurring at a time when computing power is exploding and computer memory and secondary storage are exploding in capacity while costs dramatically decline. This enables us to think differently about solution approaches.
D) All of the above statements are true.

Accepted Answer

All the statements provided accurately reflect current trends and sentiments in the field of big data, machine learning, and computing technology. Statement A highlights the exponential growth of data, Statement B discusses the shift in perspective towards big data with the advent of machine learning, and Statement C points out the significant advancements in computing power and storage capacities alongside decreasing costs.

Question 3

Which of the following statements a), b) or c) is false?
A) "Toy" datasets, generally have a small number of samples with a limited number of features. In the world of big data, datasets commonly have millions and billions of samples, or even more.
B) There's an enormous number of free and open datasets available for data science studies. Libraries like scikit-learn bundle popular datasets for you to experiment with and provide mechanisms for loading datasets from various repositories (such as openml.org).
C) Governments, businesses and other organizations worldwide offer datasets on a vast range of subjects.
D) All of the above statements are true.

Accepted Answer

All three statements are true. Toy datasets are smaller in size, there are many free and open datasets available for data science, and various organizations offer datasets on different subjects.

Question 4

With regard to our code that displays 24 digit images, which of the following statements a), b) or c) is false?
A) The following call to function subplots creates a 6-by-4 inch Figure (specified by the figsize=(6, 4) keyword argument) containing 24 subplots arranged in 6 rows and 4 columns: import matplotlib.pyplot as plt
Figure, axes = plt.subplots(nrows=4, ncols=6, figsize=(6, 4))
B) Each subplot has its own Axes object.
C) Function subplots returns the Axes objects in a two-dimensional NumPy array.
D) All of the above are true.

Accepted Answer

B and C are correct statements. A is false because the call to plt.subplots incorrectly specifies the number of rows and columns; it should be `nrows=6, ncols=4` for 24 subplots arranged in 6 rows and 4 columns, not `nrows=4, ncols=6` as stated.

Question 5

Which of the following statements is false?
A) K-means clustering works through the data attempting to divide it into that many clusters. 
B) As with many machine learning algorithms, k-means clustering is recursive and gradually zeros in on the clusters to match the number you specify. 
C) K-means clustering can find similarities in unlabeled data. This can ultimately help with assigning labels to that data so that supervised learning estimators can then process it. 
D) Given that it’s tedious and error-prone for humans to have to assign labels to unlabeled data, and given that the vast majority of the world’s data is unlabeled, unsupervised machine learning is an important tool.

Accepted Answer

K-means clustering is an iterative algorithm, not recursive. It updates the cluster centroids in each iteration based on the mean of the points assigned to each cluster until convergence, rather than calling itself recursively.

Question 6

Which of the following statements about scikit-learn and the machine-learning models you'll build with it is false?
A) It's difficult to know in advance which model(s) will perform best on your data, so you typically try many models and pick the one that performs best-scikit-learn makes this convenient for you.
B) You'll rarely get to know the details of the complex mathematical algorithms in the scikit-learn estimators, but with experience, you'll be able to intuit the best model for each new dataset.
C) It generally takes at most a few lines of code for you to create and use each scikit-learn model.
D) The models report their performance so you can compare the results and pick the model(s) with the best performance.

Accepted Answer

Scikit-learn is designed to be accessible and to abstract away the complexities of the underlying algorithms, making it easier for users to apply machine learning models without needing deep mathematical knowledge. However, understanding the details of the algorithms can significantly enhance a user's ability to choose and tune models effectively. The statement suggests that users will rarely know the details and rely on intuition, which is misleading as gaining insight into the algorithms is encouraged and beneficial for effective model selection and application.

Question 7

Which of the following statements is false?
A) Classification in xe "supervised machine learning"supervised machine learning attempts to predict the distinct class to which a sample belongs.
B) If you have images of dogs and images of cats, you can classify each image as a "dog" or a "cat." This is a binary classification problem.
C) When classifying digit images from the Digits dataset bundled with xe "machine learning:scikit-learn"xe "scikit-learn (sklearn) machine-learning library"scikit-learn, our goal is to predict which digit an image represents. Since there are 10 possible digits (the classes), this is a multi-classification problem.
D) You train a classification model using unlabeled data.

Accepted Answer

It is not possible to train a classification model using unlabeled data because in classification, we need to have labeled data to train the model.

Question 8

Which of the following statements is false?
A) Regression models predict a continuous output, such as the predicted temperature output in a weather time-series analysis.
B) The LinearRegression estimator can perform simple linear regression.
C) The LinearRegression estimator also can perform multiple linear regression.
D) The LinearRegression estimator, by default, uses all the nonnumerical features in a dataset to make more sophisticated predictions than you can with a single-feature simple linear regression.

Accepted Answer

The LinearRegression estimator requires numerical features to perform predictions. It cannot directly use nonnumerical (categorical) features without preprocessing them into a numerical format, such as through one-hot encoding.

Question 9

Which of the following statements is false?
A) Scikit-learn's machine-learning algorithms require samples to be stored in a one-dimensional array of floating-point values (or one-dimensional array-like collection, such as a list).
B) To represent every sample as one row, multi-dimensional data must be flattened into a one-dimensional array.
C) If you work with a dataset containing categorical features (typically represented as strings, such as 'spam' or 'not-spam'), you have to preprocess those features into numerical valuesxe "one-hot encoding[one hot encoding]".
D) Scikit-learn's sklearn.preprocessing module provides capabilities for converting categorical data to numeric data.

Accepted Answer

A) Scikit-learn algorithms require input data to be in the form of a two-dimensional array (or array-like structure), where rows represent samples and columns represent features, not a one-dimensional array.

Question 10

Which of the following statements a), b) or c) is false?
A) The simplest supervised machine-learning algorithm we use is k-means clustering.
B) In k-means clustering, each cluster's centroid is the cluster's center point.
C) You'll often run multiple clustering estimators to compare their ability to divide a dataset's samples effectively into clusters.
D) All of the above statements are true.

Accepted Answer

K-means clustering is actually an unsupervised machine learning algorithm, not a supervised one.

Question 11

Which of the following statements a), b) or c) is false?
A) Scikit-learn conveniently packages the most effective machine-learning algorithms as evaluators.
B) Each scikit-learn algorithm is encapsulated, so you don't see its intricate details, including any heavy mathematics.
C) With scikit-learn and a small amount of Python code, you can create powerful models quickly for analyzing data, extracting insights from the data and making predictions.
D) All of the above statements are true.

Accepted Answer

The answer of Which of the following statements a), b)...

Question 12

Which of the following statements a), b) or c) is false?
A) We can make machines learn.
B) The "secret sauce" of machine learning is data-and lots of it.
C) With machine learning, rather than programming expertise into our applications, we program them to learn from data.
D) All of the above statements are true.

Accepted Answer

All of the statements are true.

Question 13

Which of the following statements is false?
A) Scikit-learn supports many classification algorithms, including the simplest-k-nearest neighbors (k-NN).
B) The k-nearest neighbors algorithm attempts to predict a test sample's class by looking at the k training samples that are nearest (in distance) to the test sample.
C) Always pick an even value of k for the k-nearest neighbors algorithm.
D) In the k-nearest neighbors algorithm, the class with the most "votes" wins.

Accepted Answer

It is not always necessary to pick an even value of k for the k-nearest neighbors algorithm. The choice of k depends on the dataset and should be determined using techniques like cross-validation. The other statements (A, B, and D) are true.

Question 14

Unsupervised machine learning uses ________ algorithms.
A) classification
B) clustering
C) regression
D) None of the above

Accepted Answer

Unsupervised machine learning uses clustering algorithms to group similar data points together without any predefined labels or categories. Classification and regression algorithms require labeled data for training and are therefore not used in unsupervised learning.

Question 15

Which of the following statements is false?
A) With scikit-learn, you train each model on a subset of your data, then test each model on the rest to see how well your model works.
B) Once your models are trained, you put them to work making predictions based on data they have not seen.
C) With machine learning, your computer will take on characteristics of intelligence.
D) Although you can specify parameters to customize scikit-learn models and possibly improve their performance, if you use the models' default parameters for simplicity, you'll generally obtain mediocre results.

Accepted Answer

This statement is false because scikit-learn models generally have well-chosen default parameters that can produce good results without customization.

Question 16

Which of the following statements is false?
A) The two main types of machine learning are xe "supervised machine learning"supervised machine learning, which works with unxe "labeled data"labeled data, and xe "unsupervised machine learning"unsupervised machine learning, which works with xe "unlabeled data"labeled data.
B) If you're developing a computer vision application to recognize dogs and cats, you'll train your model on lots of dog photos labeled "dog" and cat photos labeled "cat." If your model is effective, when you put it to work processing unlabeled photos it will recognize dogs and cats it has never seen before. The more photos you train with, the greater the chance that your model will accurately predict which new photos are dogs and which are cats.
C) In this era of big data and massive, economical computer power, you should be able to build some pretty accurate machine learning models.

Accepted Answer

A) is false because supervised machine learning works with labeled data, not unlabeled data, and unsupervised machine learning works with unlabeled data, not labeled data.

Question 17

Which of the following are not steps in a typical machine-learning case study?
A) loading the dataset and exploring the data with pandas and visualizations
B) transforming your data (converting non-numeric data to numeric data because scikit-learn requires numeric data) and splitting the data for training and testing
C) creating, training and testing the model; tuning the model, evaluating its accuracy and making predictions on live data that the model hasn't seen before.
D) All of the above are steps in a typical machine-learning case study.

Accepted Answer

All the options listed (A, B, and C) are indeed steps involved in a typical machine-learning case study, making D the correct choice.

Question 18

Which of the following statements is false?
A) Even though k-nearest neighbors is one of the most complex xe "classification (machine learning)"classification algorithms, because of its superior prediction accuracy we use it to analyze the Digits dataset bundled with scikit-learn.
B) Classification algorithms predict the discrete classes (categories) to which samples belong.
C) Binary classification uses two classes, such as "spam" or "not spam" in an e-mail classification application. Multi-classification uses more than two classes, such as the 10 classes, 0 through 9, in the Digits dataset.
D) A classification scheme looking at movie descriptions might try to classify them as "action," "adventure," "fantasy," "romance," "history" and the like.

Accepted Answer

The statement that k-nearest neighbors is one of the most complex classification algorithms is false. In fact, it is considered to be one of the simplest classification algorithms.

Question 19

Which of the following statements is false?
A) In machine learning, a model implements a machine-learning algorithm. In xe "machine learning:scikit-learn"xe "scikit-learn (sklearn) machine-learning library"scikit-learn, models are called estimators.
B) There are two parameter types in machine learning-those the estimator calculates as it learns from the data you provide and those you specify in advance when you create the scikit-learn estimator object that represents the model.
C) The machine-learning parameters the estimator calculates as it learns from the data are called hyperparameters-in the k-nearest neighbors algorithm, k is a hyperparameter.
D) For simplicity, we use scikit-learn's default hyperparameter values. In real-world machine-learning studies, you'll want to experiment with different values of k to produce the best possible models for your studies-this process is called hyperparameter tuning.

Accepted Answer

- The statement in C is false because the parameters that the estimator calculates as it learns from the data are not called hyperparameters; they are called model parameters. Hyperparameters are the parameters that you specify in advance before the learning process begins. In the k-nearest neighbors algorithm, k is indeed a hyperparameter, but it is not calculated from the data; it is set by the user before the model is trained.

Question 20

Which of the following are related to compressing a dataset's large number of features down to two for visualization purposes.
A) dimensionality reduction
B) TSNE estimator
C) PCA estimator
D) All of the above.

Accepted Answer

All of the provided choices are related to compressing a dataset's large number of features down to two for visualization purposes. Dimensionality reduction refers to any approach that reduces the number of dimensions in a dataset, while t-SNE (t-Distributed Stochastic Neighbor Embedding) and PCA (Principal Component Analysis) are specific methods for achieving this reduction. Both t-SNE and PCA are commonly used for visualizing high-dimensional datasets in two-dimensional space.

Quiz 15: Machine Learning: Classification, Regression and Clustering

Which of the following statements a) , b) or c) is false?

Which of the following statements a) , b) or c) is false?

Which of the following statements a) , b) or c) is false?

With regard to our code that displays 24 digit images, which of the following statements a) , b) or c) is false?

Which of the following statements is false?

Which of the following statements about scikit-learn and the machine-learning models you'll build with it is false?

Which of the following statements is false?

Which of the following statements is false?

Which of the following statements is false?

Which of the following statements a) , b) or c) is false?

Which of the following statements a) , b) or c) is false?

Which of the following statements a) , b) or c) is false?

Which of the following statements is false?

Unsupervised machine learning uses ________ algorithms.

Which of the following statements is false?

Which of the following statements is false?

Which of the following are not steps in a typical machine-learning case study?

Which of the following statements is false?

Which of the following statements is false?

Which of the following are related to compressing a dataset's large number of features down to two for visualization purposes.