On This Page
This set of Machine Learning (ML) Multiple Choice Questions & Answers (MCQs) focuses on Machine Learning Set 26
Q1 | According to____ , it’s a key success factor for the survival and evolution of all species.
- Claude Shannon\s theory
- Gini Index
- Darwin’s theory
- None of above
Q2 | What is ‘Training set’?
- Training set is used to test the accuracy of the hypotheses generated by the learner.
- A set of data is used to discover the potentially predictive relationship.
- Both A & B
- None of above
Q3 | Common deep learning applications include____
- Image classification,Real-time visual tracking
- Autonomous car driving,Logistic optimization
- Bioinformatics,Speech recognition
- All above
Q4 | Reinforcement learning is particularly efficient when______________.
- the environment is not completely deterministic
- it\s often very dynamic
- it\s impossible to have a precise error measure
- All above
Q5 | if there is only a discrete number of possible outcomes (called categories),the process becomes a______.
- Regression
- Classification.
- Modelfree
- Categories
Q6 | Which of the following are supervised learning applications
- Spam detection,Pattern detection,Natural Language Processing
- Image classification,Real-time visual tracking
- Autonomous car driving,Logistic optimization
- Bioinformatics,Speech recognition
Q7 | 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.
- Logical
- Classical
- Classification
- None of above
Q8 | What is ‘Overfitting’ in Machine learning?
- when a statistical model describes random error or noise instead of underlying relationship ‘overfitting’ occurs.
- Robots are programed so that they can perform the task based on data they gather from sensors.
- While involving the process of learning ‘overfitting’ occurs.
- a set of data is used to discover the potentially predictive relationship
Q9 | What is ‘Test set’?
- Test set is used to test the accuracy of the hypotheses generated by the learner.
- It is a set of data is used to discover the potentially predictive relationship.
- Both A & B
- None of above
Q10 | ________is much more difficult because it's necessary to determine a supervised strategy to train a model for each feature and, finally, to predict their value
- Removing the whole line
- Creating sub-model to predict those features
- Using an automatic strategy to input them according to the other known values
- All above
Q11 | How it's possible to use a different placeholder through the parameter_______.
- regression
- classification
- random_state
- missing_values
Q12 | If you need a more powerful scaling feature, with a superior control on outliers and the possibility to select a quantile range, there's also the class________.
- RobustScaler
- DictVectorizer
- LabelBinarizer
- FeatureHasher
Q13 | scikit-learn also provides a class for per-sample normalization, Normalizer. It can apply________to each element of a dataset
- max, l0 and l1 norms
- max, l1 and l2 norms
- max, l2 and l3 norms
- max, l3 and l4 norms
Q14 | There are also many univariate methods that can be used in order to select the best features according to specific criteria based on________.
- F-tests and p-values
- chi-square
- ANOVA
- All above
Q15 | ________performs a PCA with non-linearly separable data sets.
- SparsePCA
- KernelPCA
- SVD
- None of the Mentioned
Q16 | A feature F1 can take certain value: A, B, C, D, E, & F and represents grade of students from a college.Which of the following statement is true in following case?
- Feature F1 is an example of nominal variable.
- Feature F1 is an example of ordinal variable.
- It doesn’t belong to any of the above category.
- Both of these
Q17 | The parameter______ allows specifying the percentage of elements to put into the test/training set
- test_size
- training_size
- All above
- None of these
Q18 | In many classification problems, the target ______ is made up of categorical labels which cannot immediately be processed by any algorithm.
- random_state
- dataset
- test_size
- All above
Q19 | _______adopts a dictionary-oriented approach, associating to each category label a progressive integer number.
- LabelEncoder class
- LabelBinarizer class
- DictVectorizer
- FeatureHasher
Q20 | If Linear regression model perfectly first i.e., train error is zero, then _____________________
- a) Test error is also always zero
- b) Test error is non zero
- c) Couldn’t comment on Test error
- d) Test error is equal to Train error
Q21 | Function used for linear regression in R is __________
- a) lm(formula, data)
- b) lr(formula, data)
- c) lrm(formula, data)
- d) regression.linear(formula, data)
Q22 | In syntax of linear model lm(formula,data,..), data refers to ______
- a) Matrix
- b) Vector
- c) Array
- d) List
Q23 | In the mathematical Equation of Linear Regression Y = β1 + β2X + ϵ, (β1, β2) refers to __________
- a) (X-intercept, Slope)
- b) (Slope, X-Intercept)
- c) (Y-Intercept, Slope)
- d) (slope, Y-Intercept)
Q24 | Which of the following methods do we use to find the best fit line for data in Linear Regression?
- A) Least Square Error
- B) Maximum Likelihood
- C) Logarithmic Loss
- D) Both A and B
Q25 | Which of the following evaluation metrics can be used to evaluate a model while modeling a continuous output variable?
- A) AUC-ROC
- B) Accuracy
- C) Logloss
- D) Mean-Squared-Error