Machine Learning Set 19

Q1 | How many coefficients do you need to estimate in a simple linear regression model (One independent variable)?

Q2 | In a real problem, you should check to see if the SVM is separable and then include slack variables if it is not separable.

Q3 | Which of the following are real world applications of the SVM?

Q4 | 100 people are at party. Given data gives information about how many wear pink or not, and if a man or not. Imagine a pink wearing guest leaves, was it a man?

Q5 | For the given weather data, Calculate probability of playing

Q6 | In SVR we try to fit the error within a certain threshold.

Q7 | In reinforcement learning, this feedback is usually called as     .

Q8 | Which of the following sentence is correct?

Q9 | Reinforcement learning is particularly

Q10 | Lets say, you are working with categorical feature(s) and you have not looked at the distribution of the categorical variable in the test data.You want to apply one hot encoding (OHE) on the categorical feature(s). What challenges you may face if you have applied OHE on a categorical variable of train dataset?

Q11 | Which of the following sentence is FALSE regarding regression?

Q12 | Which of the following method is used to find the optimal features for cluster analysis

Q13 | scikit-learn also provides functions for creatingdummy datasets from scratch:

Q14 |          which can accept a NumPy RandomState generator or an integer seed.

Q15 | In many classification problems, the target dataset is made up of categorical labels which cannot immediately be processed by any algorithm. An encoding is needed and scikit-learn offers atleast         valid options

Q16 | In which of the following each categorical label is first turned into a positive integer and then transformed into a vector where only one feature is 1 while all the others are 0.

Q17 |            is the most drastic one and should be considered only when the dataset is quite large, the number of missing features is high, and any prediction could be risky.

Q18 | It's possible to specify if the scaling process must include both mean and standard deviation using the parameters              .

Q19 | Which of the following selects the best K high-score features.

Q20 | How does number of observations influence overfitting? Choose the correct answer(s).Note: Rest all parameters are same1. In case of fewer observations, it is easy to overfit the data.2. In case of fewer observations, it is hard to overfit the data.3. In case of more observations, it is easy to overfit the data.4. In case of more observations, it is hard to overfit the data.

Q21 | Suppose you have fitted a complex regression model on a dataset. Now, you are using Ridge regression with tuning parameter lambda to reduce its complexity. Choose the option(s) below which describes relationship of bias and variance with lambda.

Q22 | What is/are true about ridge regression?1. When lambda is 0, model works like linear regression model2. When lambda is 0, model doesnt work like linear regression model3. When lambda goes to infinity, we get very, very small coefficients approaching 04. When lambda goes to infinity, we get very, very large coefficients approaching infinity

Q23 | Which of the following method(s) does not have closed form solution for its coefficients?

Q24 | Function used for linear regression in R

Q25 | In the mathematical Equation of Linear Regression Y?=??1 + ?2X + ?, (?1, ?2) refers to