Here’s your chance to prove what you learned
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Question 1 of 8
The most common value in the dataset
The average value of the dataset
A data point that significantly differs from other observations
A data point that is exactly in the middle of the dataset
Question 2 of 8
To remove all unusual data points
To identify potential errors or significant events in the data
To make the dataset more uniform
To increase the average value of the dataset
Question 3 of 8
Defining what is "normal" for a given dataset
Dealing with noisy data
Considering contextual factors
Ensuring all data points are identical
Question 4 of 8
A defective product
A successful marketing campaign
A decrease in customer satisfaction
A problem in the supply chain
Question 5 of 8
Which of the following is NOT a statistical method for outlier detection?
Z-Score Method
Modified Z-Score Method
Tukey's Fence Method
Local Outlier Factor (LOF)
Question 6 of 8
What is the main difference between Isolation Forest and Random Forest for outlier detection?
Isolation Forest is supervised, while Random Forest is unsupervised
Isolation Forest measures separability, while Random Forest uses collective decision-making
Isolation Forest uses visualization, while Random Forest uses statistical methods
There is no difference; they are the same technique
Question 7 of 8
In the context of outlier detection, what does the Z-score represent?
The number of standard deviations a data point is from the mean
The median of the dataset
The interquartile range of the dataset
Question 8 of 8
Which visualization technique uses quartiles and interquartile range to display potential outliers?
Scatterplot
Histogram
Box Plot
Line Graph