McqMate

Q. |
## If TP=9 FP=6 FN=26 TN=70 then Error rate will be |

A. | 45 percentage |

B. | 99 percentage |

C. | 28 percentage |

D. | 20 perentage |

Answer» C. 28 percentage |

2.6k

0

Do you find this helpful?

16

View all MCQs in

Machine Learning (ML)No comments yet

- What are the steps for using a gradient descent algorithm? 1)Calculate error between the actual value and the predicted value 2)Reiterate until you find the best weights of network 3)Pass an input through the network and get values from output layer 4)Initialize random weight and bias 5)Go to each neurons which contributes to the error and change its respective values to reduce the error
- What is back propagation? a) It is another name given to the curvy function in the perceptron b) It is the transmission of error back through the network to adjust the inputs c) It is the transmission of error back through the network to allow weights to be adjusted so that the network can learn d) None of the mentioned
- If Linear regression model perfectly first i.e., train error is zero, then
- If Linear regression model perfectly first i.e., train error is zero, then
- If Linear regression model perfectly first i.e., train error is zero, then _____________________
- Which of the following methods can not achieve zero training error on any linearly separable dataset?
- Which of the following are components of generalization Error?
- We have been given a dataset with n records in which we have input attribute as x and output attribute as y. Suppose we use a linear regression method to model this data. To test our linear regressor, we split the data in training set and test set randomly. Now we increase the training set size gradually. As the training set size increases, What do you expect will happen with the mean training error?
- Suppose that we have N independent variables (X1,X2… Xn) and dependent variable is Y. Now Imagine that you are applying linear regression by fitting the best fit line using least square error on this data. You found that correlation coefficient for one of it’s variable(Say X1) with Y is 0.95.
- To control the size of the tree, we need to control the number of regions. One approach to do this would be to split tree nodes only if the resultant decrease in the sum of squares error exceeds some threshold. For the described method, which among the following are true? (a) It would, in general, help restrict the size of the trees (b) It has the potential to affect the performance of the resultant regression/classification model (c) It is computationally infeasible