Predictive Analytics Practice Questions With Answers
Ready to dive into the world of predictive analytics? Take our Predictive Analytics Quiz and discover how well you understand this powerful field! From understanding data patterns to making future predictions, this quiz covers it all. Whether you're a beginner or a pro, it's a great way to test your knowledge and learn something new about the fascinating world of predictive analytics.
By taking this quiz, individuals can assess their knowledge and proficiency in predictive analytics, identify areas for improvement, and gain insights into the practical applications of data-driven decision-making. This quiz delves into various concepts and techniques used to analyze Read morehistorical data and make informed predictions about future outcomes. Participants are presented with scenarios and questions that test their understanding of predictive modeling, machine learning algorithms, data preprocessing, feature engineering, and model evaluation methods.
Predictive Analytics Questions and Answers
- 1.
What is the main goal of predictive analytics?
- A.
To understand the past
- B.
To predict future outcomes
- C.
To describe current situations
- D.
To clean the data
Correct Answer
B. To predict future outcomesExplanation
Predictive analytics is a branch of advanced analytics that uses both new and historical data to forecast future activity, behavior, and trends. It involves applying statistical analysis techniques, analytical queries, and automated machine learning algorithms to data sets to create predictive models that place a numerical value, or score, on the likelihood of a particular event happening.Rate this question:
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- 2.
Which of the following is a commonly used predictive analytics technique?
- A.
Regression analysis
- B.
Descriptive analysis
- C.
Data cleaning
- D.
Data visualization
Correct Answer
A. Regression analysisExplanation
Regression analysis is a powerful statistical method that allows you to examine the relationship between two or more variables of interest. While many people may know how to create models, interpreting the output can be a significant challenge. In predictive analytics, it’s often used to predict and forecast future events, and can also identify the variables that influence the outcome.Rate this question:
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- 3.
What role does data cleaning play in predictive analytics?
- A.
It ensures the quality of the data used for analysis.
- B.
It predicts future outcomes.
- C.
It visualizes the data.
- D.
It estimates the relationships among variables.
Correct Answer
A. It ensures the quality of the data used for analysis.Explanation
Data cleaning is a critical step in predictive analytics. This process involves checking for errors or inconsistencies in the data and correcting or deleting them. Data cleaning can significantly impact the results of your predictive model, as the accuracy of the predictions depends heavily on the quality of the data used in the model. It ensures that the datasets used for analysis are accurate, correct, consistent, and usable.Rate this question:
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- 4.
What is the purpose of a confusion matrix in predictive analytics?
- A.
To visualize the performance of an algorithm
- B.
To clean the data
- C.
To predict future outcomes
- D.
To estimate the relationships among variables
Correct Answer
A. To visualize the performance of an algorithmExplanation
A confusion matrix, also known as an error matrix, is a specific table layout that allows visualization of the performance of an algorithm. It’s often used in supervised learning and it allows the visualization of the performance of an algorithm. Each row of the matrix represents the instances in a predicted class, while each column represents the instances in an actual class.Rate this question:
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- 5.
What is the role of feature selection in predictive analytics?
- A.
It selects the most relevant features for model building.
- B.
It cleans the data.
- C.
It visualizes the data.
- D.
It estimates the relationships among variables.
Correct Answer
A. It selects the most relevant features for model building.Explanation
Feature selection is a process where you automatically select those features in your data that contribute most to the prediction variable or output in which you are interested. Having irrelevant features in your data can decrease the accuracy of the models and make your model learn based on irrelevant features.Rate this question:
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- 6.
What is the difference between overfitting and underfitting in predictive analytics?
- A.
Overfitting is when the model performs well on the training data but poorly on the test data, while underfitting is when the model performs poorly on both.
- B.
Overfitting is when the model performs poorly on the training data but well on the test data, while underfitting is when the model performs well on both.
- C.
Overfitting is when the model performs well on both the training data and the test data, while underfitting is when the model performs poorly on the training data but well on the test data.
- D.
Overfitting is when the model performs poorly on both the training data and the test data, while underfitting is when the model performs well on the training data but poorly on the test data.
Correct Answer
A. Overfitting is when the model performs well on the training data but poorly on the test data, while underfitting is when the model performs poorly on both.Explanation
Overfitting and underfitting are the two biggest causes for poor performance of machine learning algorithms. Overfitting happens when a model learns the detail and noise in the training data to the extent that it negatively impacts the performance of the model on new data. Underfitting refers to a model that can neither model the training data nor generalize to new data.Rate this question:
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- 7.
What is cross-validation in predictive analytics?
- A.
A technique for assessing how the results of a statistical analysis will generalize to an independent data set
- B.
A technique for cleaning the data
- C.
A technique for visualizing the data
- D.
A technique for estimating the relationships among variables
Correct Answer
A. A technique for assessing how the results of a statistical analysis will generalize to an independent data setExplanation
Cross-validation is a powerful preventative measure against overfitting. The idea is clever: Use your initial training data to generate multiple mini train-test splits. Use these splits to tune your model. In standard k-fold cross-validation, we partition the data into k subsets, called folds. Then, we iteratively train the algorithm on k-1 folds while using the remaining fold as the test set.Rate this question:
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- 8.
What is the role of the AUC-ROC curve in predictive analytics?
- A.
It measures the performance of a classification model.
- B.
It cleans the data.
- C.
It visualizes the data.
- D.
It estimates the relationships among variables.
Correct Answer
A. It measures the performance of a classification model.Explanation
The AUC-ROC curve is a performance measurement for the classification problems at various threshold settings. ROC is a probability curve and AUC represents the degree or measure of separability. It tells how much the model is capable of distinguishing between classes. The higher the AUC, the better the model is at predicting 0s as 0s and 1s as 1s.Rate this question:
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- 9.
What is the purpose of the lift curve in predictive analytics?
- A.
To determine the effectiveness of a predictive model
- B.
To clean the data
- C.
To visualize the data
- D.
To estimate the relationships among variables
Correct Answer
A. To determine the effectiveness of a predictive modelExplanation
The lift curve is a popular technique in direct marketing. It measures the effectiveness of a predictive model calculated as the ratio between the results obtained with and without the predictive model. Lift is a measure of the effectiveness of a predictive model calculated as the ratio between the results obtained with and without the predictive model.Rate this question:
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- 10.
What is the difference between univariate and multivariate analysis in predictive analytics?
- A.
Univariate analysis deals with one variable, while multivariate analysis deals with more than one variable.
- B.
Univariate analysis deals with more than one variable, while multivariate analysis deals with one variable.
- C.
Univariate analysis deals with two variables, while multivariate analysis deals with three variables.
- D.
Univariate analysis deals with three variables, while multivariate analysis deals with two variables.
Correct Answer
A. Univariate analysis deals with one variable, while multivariate analysis deals with more than one variable.Explanation
Univariate analysis is the simplest form of analyzing data. It doesn’t deal with causes or relationships and its major purpose is to describe; it takes data, summarizes that data and finds patterns in the data. Multivariate analysis, on the other hand, is based on the statistical principle of multivariate statistics, which involves observation and analysis of more than one statistical outcome variable at a time.Rate this question:
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Current Version
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Mar 22, 2024Quiz Edited by
ProProfs Editorial Team -
Mar 21, 2024Quiz Created by
Kasturi Chaudhuri
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