Machine Learning Set 23

Q1 | What do you mean by generalization error in terms of the SVM?

Q2 | The effectiveness of an SVM depends upon:

Q3 | Support vectors are the data points that lie closest to the decision

Q4 | The SVM’s are less effective when:

Q5 | Suppose you are using RBF kernel in SVM with high Gamma valu

Q6 | The cost parameter in the SVM means:

Q7 | If I am using all features of my dataset and I achieve 100% accura

Q8 | Which of the following are real world applications of the SVM?

Q9 | Suppose you have trained an SVM with linear decision boundary after training SVM, you correctly infer that your SVM model is under fitting.Which of the following option would you more likely to consider iterating SVM next time?

Q10 | We usually use feature normalization before using the Gaussian k

Q11 | Linear SVMs have no hyperparameters that need to be set by cross-valid

Q12 | In a real problem, you should check to see if the SVM is separable and th

Q13 | In reinforcement learning, this feedback is usually called as .

Q14 | In the last decade, many researchers started trainingbigger and bigger models, built with several different layers that's why this approach is called .

Q15 | When it is necessary to allow the model to develop a generalization ability and avoid a common problemcalled .

Q16 | Techniques involve the usage of both labeled and unlabeled data is called .

Q17 | Reinforcement learning is particularly efficient when .

Q18 | During the last few years, many algorithms have been applied to deepneural networks to learn the best policy for playing Atari video games and to teach an agent how to associate the right action with an input representing the state.

Q19 | if there is only a discrete number of possible outcomes (called categories),the process becomes a .

Q20 | Let’s 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?

Q21 | scikit-learn also provides functions for creating dummy datasets from scratch:

Q22 |           which can accept a NumPy RandomState generator or an integer seed.

Q23 | 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

Q24 | It's possible to specify if the scaling process must include both mean and standard deviation using the parameters .

Q25 | Which of the following selects the best K high-score features.