Question 1

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

Accepted Answer

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. 
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.

Question 2

Which of the following statements is false?

Accepted Answer

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. 
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.

Question 3

Consider the following code that imports pandas and sets some options: import pandas as pd
Pd)set_option('precision', 4)
Pd)set_option('max_columns', 9)
Pd)set_option('display.width', None)
Which of the following statements a), b) or c)about the set_option calls is false?

Accepted Answer

A)  'precision' is the maximum number of digits to display to the right of each decimal point. 
B)  'max_columns' is the maximum number of columns to display when you output the DataFrame's string representation. In IPython interactive mode, by default, pandas displays all of the columns left-to-right. The 'max_columns' setting enables pandas to show all the columns using multiple rows of output. 
C)  'display.width' specifies the width in characters of your Command Prompt (Windows), Terminal (macOS/Linux) or shell (Linux). The value None tells pandas to auto-detect the display width when formatting string representations of Series and DataFrames. 
D)  All of the above statements are true. 
A)  'precision' is the maximum number of digits to display to the right of each decimal point. 
B)  'max_columns' is the maximum number of columns to display when you output the DataFrame's string representation. In IPython interactive mode, by default, pandas displays all of the columns left-to-right. The 'max_columns' setting enables pandas to show all the columns using multiple rows of output. 
C)  'display.width' specifies the width in characters of your Command Prompt (Windows), Terminal (macOS/Linux) or shell (Linux). The value None tells pandas to auto-detect the display width when formatting string representations of Series and DataFrames. 
D)  All of the above statements are true.

Question 4

Which of the following statements is false?

Accepted Answer

A)  The k in the k-nearest neighbors algorithm is a xe "machine learning:hyperparameter"hyperparameter of the algorithm. 
B)  Hyperparameters are set after using the algorithm to train your model. 
C)  In real-world machine learning studies, you'll want to use xe "hyperparameter:tuning"hyperparameter tuning to choose hyperparameter values that produce the best possible predictions. 
D)  To determine the best value for k in the kNN algorithm, try different odd values of k then compare the estimator's performance with each. 
A)  The k in the k-nearest neighbors algorithm is a xe "machine learning:hyperparameter"hyperparameter of the algorithm. 
B)  Hyperparameters are set after using the algorithm to train your model. 
C)  In real-world machine learning studies, you'll want to use xe "hyperparameter:tuning"hyperparameter tuning to choose hyperparameter values that produce the best possible predictions. 
D)  To determine the best value for k in the kNN algorithm, try different odd values of k then compare the estimator's performance with each.

Question 5

The sklearn.metrics module's xe "sklearn.metrics module:classification_report function"xe "classification_report function from the sklearn.metrics module"classification_report function produces a table of classification metrics based on the expected and predicted values, as in: from sklearn.metrics import classification_report
Names = [str(digit) for digit in digits.target_names]
Print(classification_report(expected, predicted,

Accepted Answer

A)  The precision column shows the total number of correct predictions for a given digit divided by the total number of predictions for that digit. You can confirm the precision by looking at each column in the confusion matrix. 
B)  The recall column is the total number of correct predictions for a given digit divided by the total number of samples that should have been predicted as that digit. You can confirm the recall by looking at each row in the confusion matrix. 
C)  The f1-score column is the average of the precision. The recall and the support column is the number of samples with a given expected value-for example, 50 samples were labeled as 4s, and 38 samples were labeled as 5s. 
D)  All of the above are true. 
A)  The precision column shows the total number of correct predictions for a given digit divided by the total number of predictions for that digit. You can confirm the precision by looking at each column in the confusion matrix. 
B)  The recall column is the total number of correct predictions for a given digit divided by the total number of samples that should have been predicted as that digit. You can confirm the recall by looking at each row in the confusion matrix. 
C)  The f1-score column is the average of the precision. The recall and the support column is the number of samples with a given expected value-for example, 50 samples were labeled as 4s, and 38 samples were labeled as 5s. 
D)  All of the above are true.

Question 6

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

Accepted Answer

A)  Scikit-learn has separate classes for simple linear regression and multiple linear regression. 
B)  To find the best fitting regression line for the data in a simple linear regression, a LinearRegression estimator iteratively adjusts the slope and intercept values to minimize the sum of the squares of the data points' distances from the line. 
C)  Once LinearRegression is finished performing a simple linear regression, you can use the slope and intercept in the y = mx + b calculation to make predictions. The slope is stored in the estimator's coeff_ attribute (m in the equation) and the intercept is stored in the estimator's intercept_ attribute (b in the equation). 
D)  All of the above are true. 
A)  Scikit-learn has separate classes for simple linear regression and multiple linear regression. 
B)  To find the best fitting regression line for the data in a simple linear regression, a LinearRegression estimator iteratively adjusts the slope and intercept values to minimize the sum of the squares of the data points' distances from the line. 
C)  Once LinearRegression is finished performing a simple linear regression, you can use the slope and intercept in the y = mx + b calculation to make predictions. The slope is stored in the estimator's coeff_ attribute (m in the equation) and the intercept is stored in the estimator's intercept_ attribute (b in the equation). 
D)  All of the above are true.

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

Which of the following statements is false?

Consider the following code that imports pandas and sets some options: import pandas as pd Pd)set_option('precision', 4) Pd)set_option('max_columns', 9) Pd)set_option('display.width', None) Which of the following statements a), b) or c)about the set_option calls is false?

Which of the following statements 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

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Exam 15: Machine Learning: Classification, Regression and Clustering

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

Which of the following statements is false?

Consider the following code that imports pandas and sets some options: import pandas as pd Pd)set_option('precision', 4) Pd)set_option('max_columns', 9) Pd)set_option('display.width', None) Which of the following statements a), b) or c)about the set_option calls is false?

Which of the following statements 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