McqMate

Q. |
## What characterize is hyperplance in geometrical model of machine learning? |

A. | a plane with 1 dimensional fewer than number of input attributes |

B. | a plane with 2 dimensional fewer than number of input attributes |

C. | a plane with 1 dimensional more than number of input attributes |

D. | a plane with 2 dimensional more than number of input attributes |

Answer» B. a plane with 2 dimensional fewer than number of input attributes |

2.1k

2

Do you find this helpful?

0

View all MCQs in

Machine Learning (ML)8 months ago

correct answer A) a plane with 1 dimensional fewer than number of input attributes

0

- Which of the following are correct statement(s) about stacking? 1. A machine learning model is trained on predictions of multiple machine learning models 2. A Logistic regression will definitely work better in the second stage as compared to other classification methods 3. First stage models are trained on full / partial feature space of training data
- Which of the following are correct statement(s) about stacking? A machine learning model is trained on predictions of multiple machine learning models A Logistic regression will definitely work better in the second stage as compared to other classification methods First stage models are trained on full / partial feature space of training data
- What characterize unlabeled examples in machine learning
- Machine learning techniques differ from statistical techniques in that machine learning methods
- Machine learning techniques differ from statistical techniques in that machine learning methods
- If machine learning model output involves target variable then that model is called as
- If machine learning model output doesnot involves target variable then that model is called as
- In machine learning, an algorithm (or learning algorithm) is said to be unstable if a small change in training data cause the large change in the learned classifiers. True or False: Bagging of unstable classifiers is a good idea
- A company has build a kNN classifier that gets 100% accuracy on training data. When they deployed this model on client side it has been found that the model is not at all accurate. Which of the following thing might gone wrong? Note: Model has successfully deployed and no technical issues are found at client side except the model performance
- What is/are true about ridge regression? 1. When lambda is 0, model works like linear regression model 2. When lambda is 0, model doesn't work like linear regression model 3. When lambda goes to infinity, we get very, very small coefficients approaching 0 4. When lambda goes to infinity, we get very, very large coefficients approaching infinity