## Data Mining Quiz 3

Which of the following statement is true about Bayes classifier?

(a) It always provides zero error when class distributions are known

(b) It always provides the lowest possible error when class distributions are known

(a) It always provides zero error when class distributions are known

(b) It always provides the lowest possible error when class distributions are known

(b) It always provides the lowest possible error when class distributions are known

Let A be an example, and C be a class. The probability P(C|A) is known as:

Aposteriori probability

Let A be an example, and C be a class. The probability P(C) is known as:

Apriori probability

Consider a binary classification problem with two classes C1 and C2. Class labels of ten other training set instances sorted in increasing order of their distance to an instance x are as follows: {C1, C2, C1, C2, C2, C2, C1, C2, C1, C2}. How will a K=3 nearest neighbor classifier classify x?

C1

Which of the following will be Euclidean Distance between the two data point A(1,3) and B(2,3)?

1

Which of the following will be Manhattan Distance between the two data point A(1,3) and B(2,3)?

1

What would be the class if 7-NN is used?

Class –

Support vector machine is:

Maximum margin classifier

Support vectors in SVM are:

Subset of training data points

In a hard margin support vector machine:

No training instances lie inside the margin

The Lagrange multipliers corresponding to the support vectors have a value:

greater than zero

The primal optimization problem solved to obtain the hard margin optimal separating hyperplane is:

Minimize
½ W^{T}W,
such that
y_{i}(W^{T} * X_{i} + b) >= 1
for all
i

The dual optimization problem solved to obtain the hard margin optimal separating hyperplane is:

Minimize ½ W^{T}W – Σα_{i} (y_{i} (W^{T}X_{i} + b) – 1),
such that α_{i} >= 0 for all i

The dual optimization problem in SVM design is usually solved using:

Quadratic programming

Slack variables are used in :

Soft margin SVM

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