#
*Machine
learning quiz questions TRUE or FALSE with answers, important machine
learning interview questions for data science, Top 5 machine learning
question set*

##
__Machine Learning TRUE / FALSE Questions - SET 09__

###
*1.
Variance of a model typically decreases as the number of features increases.*

*1. Variance of a model typically decreases as the number of features increases.*

(a)
TRUE (b) FALSE

**View Answer**Answer: FALSEYou can reduce
High variance, by reducing the number of features in the model. There are
several methods available to check which features don’t add much value to the
model and which are of importance. Increasing the size of the training set
can also help the model generalize. Decreasing the degree of the polynomial
can help decrease the model complexity and fix the problem of high variance. |

###
*2.
In stochastic gradient descent, we take steps in the exact direction of the
gradient vector.*

*2. In stochastic gradient descent, we take steps in the exact direction of the gradient vector.*

(a)
TRUE (b) FALSE

**View Answer**Answer: FALSEIn stochastic
gradient descent, we take steps in the opposite
direction of the gradient vector. |

###
*3.
In kernelized SVMs, the kernel matrix K has to be positive definite.*

*3. In kernelized SVMs, the kernel matrix K has to be positive definite.*

(a)
TRUE (b) FALSE

**View Answer**Answer: FALSEIn kernelized
SVMs, the kernel matrix K has to be positive
semi-definite.The kernel
function in a standard SVM produces a similarity kernel matrix over samples,
which is required to be positive semi-definite (needs to have non-negative
eigen values). This positive semi-definite property of the kernel matrix
ensures the SVMs can be efficiently solved using convex quadratic
programming. Asymmetric matrix
is positive semi-definite, if its eigen values are all non-negative. |

**************************

###
**Related links:**

**Related links:**

## No comments:

## Post a comment