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This set of Machine Learning (ML) Multiple Choice Questions & Answers (MCQs) focuses on Machine Learning Set 28

Q1 | A supervised scenario is characterized by the concept of a _____.
Q2 | overlearning causes due to an excessive ______.
Q3 | Which of the following are several models for feature extraction
Q4 | _____ provides some built-in datasets that can be used for testing purposes.
Q5 | While using _____ all labels areturned into sequential numbers.
Q6 | _______produce sparse matrices of real numbers that can be fed into any machine learning model.
Q7 | scikit-learn offers the class______, which is responsible for filling the holes using a strategy based on the mean, median, or frequency
Q8 | Which of the following scale data by removing elements that don't belong to a given range or by considering a maximum absolute value.
Q9 | ______dataset with many features contains information proportional to the independence of all features and their variance.
Q10 | The_____ parameter can assume different values which determine how the data matrix is initially processed.
Q11 | ______allows exploiting the natural sparsity of data while extracting principal components.
Q12 | Which of the following statement is true about outliers in Linear regression?
Q13 | Suppose you plotted a scatter plot between the residuals and predicted values in linear regression and you found that there is a relationship between them. Which of the following conclusion do you make about this situation?
Q14 | Let’s say, a “Linear regression” model perfectly fits the training data (train error is zero). Now, Which of the following statement is true?
Q15 | In a linear regression problem, we are using “R-squared” to measure goodness-of-fit. We add a feature in linear regression model and retrain the same model.Which of the following option is true?
Q16 | To test linear relationship of y(dependent) and x(independent) continuous variables, which of the following plot best suited?
Q17 | which of the following step / assumption in regression modeling impacts the trade-off between under-fitting and over-fitting the most.
Q18 | Which of the following statement(s) can be true post adding a variable in a linear regression model?1. R-Squared and Adjusted R-squared both increase2. R-Squared increases and Adjusted R-squared decreases3. R-Squared decreases and Adjusted R-squared decreases4. R-Squared decreases and Adjusted R-squared increases
Q19 | What is/are true about kernel in SVM?1. Kernel function map low dimensional data to high dimensional space2. It’s a similarity function
Q20 | Suppose you are building a SVM model on data X. The data X can be error prone which means that you should not trust any specific data point too much. Now think that you want to build a SVM model which has quadratic kernel function of polynomial degree 2 that uses Slack variable C as one of it’s hyper parameter.What would happen when you use very small C (C~0)?
Q21 | The cost parameter in the SVM means:
Q22 | How do you handle missing or corrupted data in a dataset?