CDS501test2_在线真题试卷与模拟练习_CDS501test2

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You are working on a project to analyze customer feedback data for a large online retail company. The company wants to categorize customer feedback into different topics to gain insights and improve customer satisfaction. Based on the scenario described above, which approach would be most suitable for categorizing customer feedback into different topics.

When it comes to model validation, what is the primary goal of generating an assurance that the model will work in production as well as it worked during training?

You are working on a project to develop a recommendation system for movies based on user preferences. You decide to use the KNN algorithm for movie recommendation. Which of the following statements best describes the KNN algorithm in this scenario?

You are working on a medical diagnosis project where you need to classify skin lesions into different categories such as benign, malignant, and uncertain. You decide to use the Naive Bayes algorithm for classification. Which of the following statements is true regarding the Naive Bayes algorithm in this scenario?

Select the statement(s) that represent why in certain cases, accuracy can be excluded as a performance metric for classification evaluation.

I. Measuring accuracy is sometimes difficult.

II. High accuracy model sometimes would not imply it is a good model for an imbalanced dataset.

III. Accuracy does not calculate the opposite of an error.

IV. Accuracy is sometimes not relevant for critical model.

Identify the steps that are carried out in the inner-loop of the k-means iterative algorithm.

I. Initiate the cluster centroids randomly

II. Assign the data point to the nearest cluster center

III. Update or recalculate the cluster centroids

IV. Reassign each data point to new clusters

Select the statement(s) that do not accurately represent the challenges that make Natural Language Processing (NLP) difficult.

I. Able to understand the sentiment of this tweet:

@Sony and @PlayStation said this would be the most accessible console of them all. Yeah right.

II. Transforming the unstructured non-text data into text data

III. Tokenizing the words into smaller units

IV. When the word "I saw her duck" can be a noun or verb.

In a linear regression model, the observed values (y) for a set of data points are [10, 15, 12], and the corresponding predicted values are [12, 14, 10]. What are the residuals for these data points?

Consider the following confusion matrix to predict images of Apple, Peach, and Tomatoes. Calculate the accuracy of the model and precision value for each class.

Confusion matrix

True values

_ Apple Peach Tomatoes

Predicted

Values

Apple 15 1 4

Peach 2 9 2

Tomatoes 3 3 11

You are working on a project that involves sentiment analysis of customer feedback for a new mobile application. The goal is to categorize the feedback into positive, negative, or neutral sentiments. Your team is considering the use of lexicon-based approaches for sentiment analysis. Which of the following statements is true regarding lexicon-based approaches in the context of sentiment analysis for customer feedback?

Imagine you are a data analyst at a retail company, and you want to predict which products are likely to sell well during the holiday season. What is the purpose of cross-validation in this scenario?

A real estate agency wants to develop a predictive model to estimate the sale prices of houses based on various attributes such as location, square footage, number of bedrooms, and presence of amenities (e.g., swimming pool, garage). Considering the given case study, which evaluation metric would be most appropriate for assessing the accuracy of the regression model in predicting house prices?

You are analyzing a dataset containing information about housing prices. The dataset includes various features such as the number of bedrooms, square footage, and location. You want to build a model to predict the price of a house based on these features. However, you suspect that some of the features may be highly correlated with each other. Which of the following techniques would be the most suitable to handle the given scenario?

You are working on a project to analyze customer reviews for a product. You want to group similar reviews together to identify common themes and sentiments. However, you do not have any predefined categories or labels for the reviews. Which of the following techniques would be most suitable for grouping similar customer reviews without any predefined categories or labels?

What does the coefficient of determination (R-squared) measure in regression analysis?

In logistic regression, what is the purpose of the categorical response variable?

In a simple linear regression model, the correlation coefficient (r) between the independent variable (x) and the dependent variable (y) is 0.8. What is the coefficient of determination (R-squared)?

Which type of logistic regression would be most appropriate for predicting which food is preferred more among three categories: Veg, Non-Veg, and Vegan?

Consider the following scenario: You are analyzing customer reviews for a new smartphone. While the overall sentiment of the document may be positive, some individual sentences within the document may express negative opinions about specific features. Which classification level would be most suitable for identifying these nuanced sentiments?

Which of the following is a limitation of association rule mining?

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