ML Quiz 11

Which of the following is an example of a deterministic algorithm?

• PCA

• K-Means

• All of them

• None

Which of the following hyper parameter(s), when increased may cause random forest to over fit the data?

1. Number of Trees

2. Depth of Tree

3. Learning Rate

• Only 1

• Only 2

• Only 3

• Both 1 & 2

• Both 1 & 3

Which of the following statements is/are true about “Type-1” and “Type-2” errors?

1. Type1 is known as false positive and Type2 is known as false negative.

2. Type1 is known as false negative and Type2 is known as false positive.

3. Type1 error occurs when we reject a null hypothesis when it is actually true.

• Only 1

• Only 3

• Only 2

• Both 1 and 2

• Both 1 and 3

Which of the following is a widely used and effective machine learning algorithm based on the idea of bagging?

• Decision Tree

• Regression

• Classification

• Random Forest

Which of the following is a disadvantage of decision trees?

• Factor analysis

• Decision trees are robust to outliers

• Decision trees are prone to be overfit

• None of the above

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

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

• Data points with outliers

• Data points with different densities

• Data points with nonconvex shapes

• All of the above

Which of the following is true about the Gradient Boosting trees?

1. In each stage, introduce a new regression tree to compensate the shortcomings of existing model

2. We can use gradient decent method for minimize the loss function

• 1

• 2

• 1 & 2

• None

Minimizing loss+complexity is called empirical risk minimization

• True

• False