figure 4.3 illustrates the coverage of the classification rules r1, r2, and r3. determine which is the best and worst rule according to

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Answer 1

In order to determine the best and worst rule according to the figure 4.3 which illustrates the coverage of the classification rules r1, r2, and r3, we need to understand what classification rules are and what they do. Classification rules are used in data mining and machine learning as a way of predicting outcomes based on a set of given inputs.

They are often used in applications such as fraud detection, product recommendation, and email spam filtering.The three classification rules illustrated in figure 4.3 are r1, r2, and r3. Each rule has a different level of coverage, which means that it is able to predict outcomes for a different set of inputs. The best rule would be the one that has the highest coverage, while the worst rule would be the one that has the lowest coverage.In order to determine which rule is the best and which one is the worst, we need to look at the coverage of each rule. According to figure 4.3, r1 has the highest coverage, followed by r3 and then r2. This means that r1 is the best rule, while r2 is the worst rule. R1 has a coverage of 80%, r2 has a coverage of 60%, and r3 has a coverage of 70%.In conclusion, based on figure 4.3, the best rule according to the coverage is r1 with 80% coverage, and the worst rule is r2 with only 60% coverage. R3 has a coverage of 70%.

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TRUE/FALSE. when you get and transform data from an external source, you must add it to a worksheet without any changes.

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The statement "when you get and transform data from an external source, you must add it to a worksheet without any changes" is false.

When you import and transform data from an external source, you can add it to a worksheet with or without modifications.The following are the steps to add the transformed data to a worksheet.

Step 1: To transform the data, choose a data source and transform the data to fit your specifications in the Power Query Editor.

Step 2: Choose Close & Load from the Close & Load drop-down list in the Close & Load drop-down list in the Close & Load group on the Home tab on the Power Query Editor ribbon.

Step 3: The Load To page is where you can specify how the query results are displayed in the Excel workbook. Select Table, PivotTable Report, or PivotChart Report from the options.

Step 4: Select Existing worksheet and choose the location on the worksheet where the data should be placed from the options available.

Step 5: To finish the wizard and add the transformed data to the worksheet, click OK.This process saves a lot of time and helps to keep the data up to date with the source. Data should be updated on a regular basis to keep it current and to aid in making critical decisions based on accurate and current data.

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What does sarah Bergbreiter mean by " there's still a long way to go"?

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Without more context, it's difficult to say for sure what Sarah Bergbreiter meant by "there's still a long way to go." However, in general, this phrase is often used to mean that although some progress has been made, there is still much work to be done. It suggests that the current situation or achievement is not yet good enough or complete, and that more effort or time is needed to reach a desired goal.

In the context of robotics, Bergbreiter may be referring to the fact that although micro-robots have made significant advances in recent years, there are still many challenges that need to be overcome before they can be widely used in practical applications. These challenges could include things like improving their durability, increasing their power efficiency, and making them more reliable in complex environments. By saying that "there's still a long way to go," Bergbreiter may be emphasizing the need for continued research and development in this field.

you use the commond gmnome-sheel --replace at the command line and receive adn error message from the utility. what doest tshi indicate

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The command line utility "gnome-shell --replace" is used to replace the existing user interface with a new user interface. Receiving an error message when running this command indicates that something is preventing the new user interface from loading.

Possible causes include: insufficient memory, incompatible graphic drivers, corrupted system files, or incorrect command syntax. It is important to investigate further to determine the exact cause of the error. If the cause of the error is not immediately apparent, troubleshooting techniques like running a system file check, updating or changing graphic drivers, and using the command line log files can help pinpoint the issue.

Additionally, if the error message includes specific technical information, it is often helpful to research that information to find additional resources. Finally, it is important to be sure to always use the correct syntax when running commands. Ultimately, an error message received when running the command "gnome-shell --replace" indicates that something is preventing the new user interface from loading. Investigating further is necessary to find the cause of the issue and resolve the problem.

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in data science, is when a data analyst uses their unique past experiences to understand the story the data is telling.

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This involves the analyst using their own knowledge, skills and intuition to interpret the data and draw conclusions. They are also able to identify patterns and trends in the data that may not be immediately obvious.

What is Data science?

Data science is the study of extracting knowledge from data. It involves the application of mathematics, statistics, and computing to uncover the hidden meanings and patterns in data. Data science is a subset of the broader field of data analytics. Data science is used by companies to gain insights about their customers, markets, products, and operations.

The data analyst’s role in data science is to use their unique past experiences to understand the story the data is telling. This requires the analyst to have a deep understanding of the data and to be able to identify patterns, trends, and correlations in the data. This type of analysis requires the analyst to be creative and open-minded in order to make sense of the data. Analysts must also be able to draw meaningful conclusions from the data and make predictions about the future.

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