Machine Learning Beginner Set - 2

How do you handle missing or corrupted data in a dataset?

• Drop missing rows or columns

• Replace missing values with mean/median/mode

• Assign a unique category to missing values

• All of the above

When performing regression or classification, which of the following is the correct way to pre-process the data?

• Normalize the data → PCA →training

• PCA → normalize PCA output →training

• Normalize the data → PCA →normalize PCA output → training

• None of the above

Supervised learning and unsupervised clustering both require at least one

• Categorical Attribute

• Input Attribute

• Output Attribute

• Hidden Attribute

A measure of goodness of fit for the estimated regression equation is the

• multiple coefficient of determination

• mean square of error

• mean square due to regression

• none of the above

In which of the following cases will K-means clustering fail to give good results?

1) Data points with outliers

2) Data points with different densities

3) Data points with nonconvex shapes

• 1 and 2

• 2 and 3

• 1, 2, and 3

• 1 and 3

Adding more basis functions in a linear model... (pick the most probably option)

• decreases model bias

• decreases estimation bias

• decreases variance

• doesn’t affect bias and variance

Classification problems are distinguished from estimation problems in that

• classification problems require the output attribute to be numeric.

• classification problems require the output attribute to be categorical.

• classification problems do not allow an output attribute.

• classification problems are designed to predict future outcome.

Machine learning techniques differ from statistical techniques in that machine learning methods

• typically assume an underlying distribution for the data.

• are better able to deal with missing and noisy data.

• are not able to explain their behavior.

• have trouble with large-sized datasets

Suppose your model is overfitting. Which of the following is NOT a valid way to try and reduce the overfitting?

• increase the amount of training data.

• improve the optimization algorithm being used for error minimization.

• decrease the model complexity.

• reduce the noise in the training data.

The correlation coefficient for two attributes is 0.90. What does this value tell you?

• the attributes are not linearly related.

• as the value of one attribute increases the value of the second attribute also increases.

• as the value of one attribute decreases the value of the second attribute increases.

• the attributes show a curvilinear relationship.