Question 1

Which of the following statements is false?

Accepted Answer

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. 
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. D

Question 2

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

Accepted Answer

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. 
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. A

Question 3

Which of the following statements is false?

Accepted Answer

A)  Each centroid in the KMeans object's cluster_centers_ array has the same number of features as the original dataset (four in the case of the Iris dataset). 
B)  To plot the centroids in two-dimensions, you must reduce their dimensions. 
C)  You can think of a centroid as the "median" sample in its cluster. 
D)  Each centroid should be transformed using the same PCA estimator used to reduce the other samples in that cluster 
A)  Each centroid in the KMeans object's cluster_centers_ array has the same number of features as the original dataset (four in the case of the Iris dataset). 
B)  To plot the centroids in two-dimensions, you must reduce their dimensions. 
C)  You can think of a centroid as the "median" sample in its cluster. 
D)  Each centroid should be transformed using the same PCA estimator used to reduce the other samples in that cluster C

Question 4

Which of the following statements is false?
A. The following code tests a linear regression model using the data in X_test and checks some of the predictions throughout the dataset by displaying the predicted and expected values for every ________ element: predicted = linear_regression.predict(X_test)
Expected = y_test
For p, e in zip(predicted[::5], expected[::5]):
Print(f'predicted: {p:.2f}, expected: {e:.2f}')

Accepted Answer

A)  second 
B)  fifth 
C)  pth 
D)  eth 
A)  second 
B)  fifth 
C)  pth 
D)  eth

Question 5

Which of the following statements is false?

Accepted Answer

A)  You load the California Housing dataset using the the xe "modules:sklearn.datasets"xe "sklearn.datasets module"sklearn.datasets module's fetch_california_housing function, which returns a Bunch object. 
B)  The Bunch object's xe "Bunch class from sklearn.utils:data attribute"xe "data:attribute of a Bunch"data and xe "Bunch class from sklearn.utils:target attribute"xe "target attribute of a Bunch"target attributes are NumPy arrays containing the 20,640 xe "machine learning:samples"xe "samples (in machine learning)"samples and their xe "machine learning:target values"xe "target values (in machine learning)"target values respectively. 
C)  To confirm the number of samples (rows) and features (columns), look at the data array's shape attribute, which shows that there are 20,640 rows and 8 columns, as in: In [4]: california.data.shape
Out[4]: (20640, 8)
Similarly, you can see that the number of target values-the median house values-matches the number of samples by looking at the target array's shape, as in:
In [5]: california.target.shape
Out[5]: (20640,) 
D)  The Bunch's features attribute contains the names that correspond to each column in the data array. 
A)  You load the California Housing dataset using the the xe "modules:sklearn.datasets"xe "sklearn.datasets module"sklearn.datasets module's fetch_california_housing function, which returns a Bunch object. 
B)  The Bunch object's xe "Bunch class from sklearn.utils:data attribute"xe "data:attribute of a Bunch"data and xe "Bunch class from sklearn.utils:target attribute"xe "target attribute of a Bunch"target attributes are NumPy arrays containing the 20,640 xe "machine learning:samples"xe "samples (in machine learning)"samples and their xe "machine learning:target values"xe "target values (in machine learning)"target values respectively. 
C)  To confirm the number of samples (rows) and features (columns), look at the data array's shape attribute, which shows that there are 20,640 rows and 8 columns, as in: In [4]: california.data.shape
Out[4]: (20640, 8)
Similarly, you can see that the number of target values-the median house values-matches the number of samples by looking at the target array's shape, as in:
In [5]: california.target.shape
Out[5]: (20640,) 
D)  The Bunch's features attribute contains the names that correspond to each column in the data array.

Question 6

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

Accepted Answer

A)  It's difficult for humans to think about data with large numbers of dimensions. This is called the curse of dimensionality. 
B)  If data has closely correlated features, some could be eliminated via dimensionality reduction to improve the training performance. 
C)  Eliminating features with dimensionality reduction, improves the accuracy of the model. 
D)  All of the above statements are true. 
A)  It's difficult for humans to think about data with large numbers of dimensions. This is called the curse of dimensionality. 
B)  If data has closely correlated features, some could be eliminated via dimensionality reduction to improve the training performance. 
C)  Eliminating features with dimensionality reduction, improves the accuracy of the model. 
D)  All of the above statements are true.

Question 7

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

Accepted Answer

A)  The LinearRegression estimator is in the sklearn.linear_model module. 
B)  By default, LinearRegression uses all the numeric features in a dataset, performing a multiple linear regression. 
C)  Simple linear regression uses one feature as the xe "independent variable"independent variable. 
D)  All of the above statements are true. 
A)  The LinearRegression estimator is in the sklearn.linear_model module. 
B)  By default, LinearRegression uses all the numeric features in a dataset, performing a multiple linear regression. 
C)  Simple linear regression uses one feature as the xe "independent variable"independent variable. 
D)  All of the above statements are true.

Question 8

Which of the following statements is false?

Accepted Answer

A)  With K-fold cross-validation, you use all of your data at once for training your model. 
B)  K-fold cross-validation splits the dataset into k equal-size folds. 
C)  You then repeatedly train your model with k - 1 folds and test the model with the remaining fold. 
D)  Consider using k = 10 with folds numbered 1 through 10. With 10 folds, we'd do 10 successive training and testing cycles: $\bullet$First, we'd train with folds 1-9, then test with fold 10.
$\bullet$Next, we'd train with folds 1-8 and 10, then test with fold 9.
$\bullet$Next, we'd train with folds 1-7 and 9-10, then test with fold 8.
This training and testing cycle continues until each fold has been used to test the model. 
A)  With K-fold cross-validation, you use all of your data at once for training your model. 
B)  K-fold cross-validation splits the dataset into k equal-size folds. 
C)  You then repeatedly train your model with k - 1 folds and test the model with the remaining fold. 
D)  Consider using k = 10 with folds numbered 1 through 10. With 10 folds, we'd do 10 successive training and testing cycles: $\bullet$First, we'd train with folds 1-9, then test with fold 10.
$\bullet$Next, we'd train with folds 1-8 and 10, then test with fold 9.
$\bullet$Next, we'd train with folds 1-7 and 9-10, then test with fold 8.
This training and testing cycle continues until each fold has been used to test the model.

Question 9

Which of the following statements is false?

Accepted Answer

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. 
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.

Question 10

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

Accepted Answer

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. 
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.

Question 11

Which of the following statements is false?

Accepted Answer

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. 
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.

Question 12

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

Accepted Answer

A)  Dimensionality reduction in scikit-learn typically involves two steps-training the estimator with the dataset, then using the estimator to transform the data into the specified number of dimensions. 
B)  The steps mentioned in Part (a) can be performed separately with the TSNE methods fit and transform, or they can be performed in one statement using the fit_transform method, as in: In [5]: reduced_data = tsne.fit_transform(digits.data) 
C)  TSNE's fit_transform method takes some time to train the estimator then perform the reduction. When the method completes its task, it returns an array with the same number of rows as digits.data, but only the number of columns specified by the n_components argument when you created the estimator object. You can confirm this by checking reduced_data's shape. 
D)  All of the above statements are true. 
A)  Dimensionality reduction in scikit-learn typically involves two steps-training the estimator with the dataset, then using the estimator to transform the data into the specified number of dimensions. 
B)  The steps mentioned in Part (a) can be performed separately with the TSNE methods fit and transform, or they can be performed in one statement using the fit_transform method, as in: In [5]: reduced_data = tsne.fit_transform(digits.data) 
C)  TSNE's fit_transform method takes some time to train the estimator then perform the reduction. When the method completes its task, it returns an array with the same number of rows as digits.data, but only the number of columns specified by the n_components argument when you created the estimator object. You can confirm this by checking reduced_data's shape. 
D)  All of the above statements are true.

Question 13

Which of the following statements is false?

Accepted Answer

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. 
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.

Question 14

Which of the following statements is false?

Accepted Answer

A)  When creating a model, a key goal is to ensure that it is capable of making accurate predictions for data it has not yet seen. Two common problems that prevent accurate predictions are overfitting and underfitting. 
B)  Underfitting occurs when a model is too simple to make accurate predictions, based on its training data. An example of underfitting is using a linear model, such as simple linear regression, when in fact, the problem really requires a more sophisticated non-linear model. 
C)  Overfitting occurs when your model is too complex. In the most extreme case of overfitting, a model memorizes its training data. 
D)  When you make predictions with an overfit model, the model won't know what to do with new data that matches the training data, but the model will make excellent predictions with data it has never seen. 
A)  When creating a model, a key goal is to ensure that it is capable of making accurate predictions for data it has not yet seen. Two common problems that prevent accurate predictions are overfitting and underfitting. 
B)  Underfitting occurs when a model is too simple to make accurate predictions, based on its training data. An example of underfitting is using a linear model, such as simple linear regression, when in fact, the problem really requires a more sophisticated non-linear model. 
C)  Overfitting occurs when your model is too complex. In the most extreme case of overfitting, a model memorizes its training data. 
D)  When you make predictions with an overfit model, the model won't know what to do with new data that matches the training data, but the model will make excellent predictions with data it has never seen.

Question 15

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

Accepted Answer

A)  The KNeighborsClassifier estimator (module sklearn.neighbors) implements the k-nearest neighbors algorithm. 
B)  The following code creates a KNeighborsClassifier estimator object: from sklearn.neighbors import KNeighborsClassifier
Knn = KNeighborsClassifier() 
C)  The internal details of how a KNeighborsClassifier object implements the k-nearest neighbors algorithm are hidden in the object. You simply call its methods. 
D)  All of the above statements are true. 
A)  The KNeighborsClassifier estimator (module sklearn.neighbors) implements the k-nearest neighbors algorithm. 
B)  The following code creates a KNeighborsClassifier estimator object: from sklearn.neighbors import KNeighborsClassifier
Knn = KNeighborsClassifier() 
C)  The internal details of how a KNeighborsClassifier object implements the k-nearest neighbors algorithm are hidden in the object. You simply call its methods. 
D)  All of the above statements are true.

Question 16

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

Accepted Answer

A)  The California Housing dataset (bundled with xe "sklearn (scikit-learn)"xe "machine learning:scikit-learn"xe "scikit-learn (sklearn) machine-learning library"scikit-learn) has 20,640 samples, each with eight numerical features. 
B)  The LinearRegression estimator performs multiple linear regression by default using all of a dataset's numeric features. 
C)  You should expect more meaningful results from simple linear regression than from multiple linear regression on the dataset. 
D)  All of the above statements are true. 
A)  The California Housing dataset (bundled with xe "sklearn (scikit-learn)"xe "machine learning:scikit-learn"xe "scikit-learn (sklearn) machine-learning library"scikit-learn) has 20,640 samples, each with eight numerical features. 
B)  The LinearRegression estimator performs multiple linear regression by default using all of a dataset's numeric features. 
C)  You should expect more meaningful results from simple linear regression than from multiple linear regression on the dataset. 
D)  All of the above statements are true.

Question 17

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

Accepted Answer

A)  It's helpful to xe "visualize the data"visualize your data by plotting the target value against each feature-in the case of the California Housing Prices dataset, to see how the median home value relates to each feature. 
B)  DataFrame method sample can randomly select a percentage of a DataFrame's data (specified keyword argument frac), as in: sample_df = california_df.sample(frac=0.1, random_state=17) 
C)  The keyword argument random_state in Part (b)'s snippet enables you to seed the random number generator. Each time you use the same seed value, method sample selects a similar random subset of the DataFrame's rows. 
D)  All of the above statements are true. 
A)  It's helpful to xe "visualize the data"visualize your data by plotting the target value against each feature-in the case of the California Housing Prices dataset, to see how the median home value relates to each feature. 
B)  DataFrame method sample can randomly select a percentage of a DataFrame's data (specified keyword argument frac), as in: sample_df = california_df.sample(frac=0.1, random_state=17) 
C)  The keyword argument random_state in Part (b)'s snippet enables you to seed the random number generator. Each time you use the same seed value, method sample selects a similar random subset of the DataFrame's rows. 
D)  All of the above statements are true.

Question 18

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

Accepted Answer

A)  You should first break your data into a training set and a testing set to prepare to train and test a model. 
B)  The function train_test_split from the sklearn.model_selection module simply splits in order the dataset's samples and target values into training and testing sets. This helps ensure that the training and testing sets have similar characteristics. 
C)  Function train_test_split provides the keyword argument random_state for xe "reproducibility"reproducibility. When you run the code in the future with the same seed value, train_test_split will select the same data for the training set and the same data for the testing set. In machine-learning studies, this helps others confirm your results by working with the same randomly selected data. 
D)  All of the above statements are true. 
A)  You should first break your data into a training set and a testing set to prepare to train and test a model. 
B)  The function train_test_split from the sklearn.model_selection module simply splits in order the dataset's samples and target values into training and testing sets. This helps ensure that the training and testing sets have similar characteristics. 
C)  Function train_test_split provides the keyword argument random_state for xe "reproducibility"reproducibility. When you run the code in the future with the same seed value, train_test_split will select the same data for the training set and the same data for the testing set. In machine-learning studies, this helps others confirm your results by working with the same randomly selected data. 
D)  All of the above statements are true.

Question 19

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

Accepted Answer

A)  The following call to the KNeighborsClassifier object's fit method loads the training set's samples (X_train) and targets (y_train) into the estimator: knn.fit(X=X_train, y=y_train) 
B)  After the KNeighborsClassifier's fit method loads the data into the estimator, it uses that data to perform complex calculations behind the scenes that learn from the data and train the model. 
C)  The KNeighborsClassifier estimator is said to be xe "lazy estimator (scikit-learn)"lazy because its work is performed only when you use it to make predictions. 
D)  All of the above statements are true. 
A)  The following call to the KNeighborsClassifier object's fit method loads the training set's samples (X_train) and targets (y_train) into the estimator: knn.fit(X=X_train, y=y_train) 
B)  After the KNeighborsClassifier's fit method loads the data into the estimator, it uses that data to perform complex calculations behind the scenes that learn from the data and train the model. 
C)  The KNeighborsClassifier estimator is said to be xe "lazy estimator (scikit-learn)"lazy because its work is performed only when you use it to make predictions. 
D)  All of the above statements are true.

Question 20

Scikit-learn estimators require their training and testing data to be two-dimensional arrays (or two-dimensional array-like data, such as lists of lists or pandas DataFrames). Which of the following statements is false?
a. To transform a one-dimensional array into two dimensions, we call an array's ________ method.

Accepted Answer

A)  transform 
B)  switch 
C)  convert 
D)  reshape 
A)  transform 
B)  switch 
C)  convert 
D)  reshape

Which of the following statements is false?

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

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

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

Introduction to Computers and Python

Introduction to Python Programming

Control Statements and Program Development

Functions

Sequences: Lists and Tuples

Dictionaries and Sets

Array-Oriented Programming With Num

Strings: a Deeper Look

Files and Exceptions

Object-Oriented Programming

Computer Science Thinking: Recursion, Searching, Sorting and Big O

Natural Language Processing

Data Mining Twitter

Ibm Watson and Cognitive Computing

Deep Learning

Big Data: Hadoop, Spark, Nosql and Iot

Filters

Exam 15: Machine Learning: Classification, Regression and Clustering

Which of the following statements is false?

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

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

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

Introduction to Computers and Python

Introduction to Python Programming

Control Statements and Program Development

Functions

Sequences: Lists and Tuples

Dictionaries and Sets

Array-Oriented Programming With Num

Strings: a Deeper Look

Files and Exceptions

Object-Oriented Programming

Computer Science Thinking: Recursion, Searching, Sorting and Big O

Natural Language Processing

Data Mining Twitter

Ibm Watson and Cognitive Computing

Deep Learning

Big Data: Hadoop, Spark, Nosql and Iot

Filters