McqMate

Q. |
## Logistic regression is a ________ regression technique that is used to model data having a _____outcome. |

A. | linear, numeric |

B. | linear, binary |

C. | nonlinear, numeric |

D. | nonlinear, binary |

Answer» D. nonlinear, binary |

2.6k

0

Do you find this helpful?

20

View all MCQs in

Machine Learning (ML)No comments yet

- 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
- 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
- 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
- Which of the following methods do we use to best fit the data in Logistic Regression?
- Generally, which of the following method(s) is used for predicting continuous dependent variable?1. Linear Regression2. Logistic Regression
- Which of the following evaluation metrics can not be applied in case of logistic regression output to compare with target?
- In a linear regression problem, we are using �R-squared� to measure goodness-of-fit. We add a feature in linear regression model and retrain the same model.Which of the following option is true?
- In a linear regression problem, we are using “R-squared” to measure goodness-of-fit. We add a feature in linear regression model and retrain the same model.Which of the following option is true?
- 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