100% Free PCED Practice Exam Questions and Answers

What is the primary reason to use PCA (Principal Component Analysis) over t-SNE when visualizing high-dimensional data?

You have a dataset with 15 features and you want to visualize it on a 2D plot for exploratory data analysis. What would be the first step in using PCA (Principal Component Analysis) to achieve this?

You have a DataFrame named df that has three columns: 'A', 'B', 'C'. The DataFrame has 100 rows. Your task is to create a DataFrame where each column 'A', 'B', 'C' is transformed into 3 columns each: 'A_min', 'A_max', 'A_mean', 'B_min', 'B_max', 'B_mean', 'C_min', 'C_max', 'C_mean'. Which of the following code snippets accomplishes this task?

While evaluating a logistic regression model using a confusion matrix, you notice that the True Positive rate is significantly higher than the True Negative rate. What does this observation imply?

Your dataset consists of geographical data points for different species of birds. You want to visualize the distribution of these species on a geographical map. Which Python library would be most appropriate for this kind of visualization?