Machine Learning Set 15

Computer Science Engineering (CSE)

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This set of Machine Learning (ML) Multiple Choice Questions & Answers (MCQs) focuses on Machine Learning Set 15

Q1 | What does learning exactly mean?
Q2 | When it is necessary to allow the model to develop a generalization ability and avoid a common problem called           .
Q3 | Techniques involve the usage of both labeled and unlabeled data is called     .
Q4 | In reinforcement learning if feedback is negative one it is defined as       .
Q5 | A supervised scenario is characterized by the concept of a          .
Q6 | overlearning causes due to an excessive           .
Q7 | Which of the following is an example of a deterministic algorithm?
Q8 | Which of the following model model include a backwards elimination feature selection routine?
Q9 | Can we extract knowledge without apply feature selection
Q10 | While using feature selection on the data, is the number of features decreases.
Q11 | Which of the following are several models
Q12 |           provides some built-in datasets that can be used for testing purposes.
Q13 | While using           all labels are turned into sequential numbers.
Q14 |              produce sparse matrices of real numbers that can be fed into any machine learning model.
Q15 | scikit-learn offers the class           , which is responsible for filling the holes using a strategy based on the mean, median, or frequency
Q16 | Which of the following scale data by removing elements that don't belong to a given range or by considering a maximum absolute value.
Q17 | scikit-learn also provides a class for per- sample normalization,          
Q18 |            dataset with many features contains information proportional to the independence of all features and their variance.
Q19 | In order to assess how much information is brought by each component, and the correlation among them, a useful tool is the         .
Q20 | The          parameter can assume different values which determine how the data matrix is initially processed.
Q21 |            allows exploiting the natural sparsity of data while extracting principal components.
Q22 | Which of the following is true about Residuals ?
Q23 | Overfitting is more likely when you have huge amount of data to train?
Q24 | Which of the following statement is true about outliers in Linear regression?
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