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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?
- robots are programed so that they can perform the task based on data they gather from sensors.
- a set of data is used to discover the potentially predictive relationship.
- learning is the ability to change according to external stimuli and remembering most of all previous experiences.
- it is a set of data is used to discover the potentially predictive relationship.
Q2 | When it is necessary to allow the model to develop a generalization ability and avoid a common problem called .
- overfitting
- overlearning
- classification
- regression
Q3 | Techniques involve the usage of both labeled and unlabeled data is called .
- supervised
- semi-supervised
- unsupervised
- none of the above
Q4 | In reinforcement learning if feedback is negative one it is defined as .
- penalty
- overlearning
- reward
- none of above
Q5 | A supervised scenario is characterized by the concept of a .
- programmer
- teacher
- author
- farmer
Q6 | overlearning causes due to an excessive .
- capacity
- regression
- reinforcement
- accuracy
Q7 | Which of the following is an example of a deterministic algorithm?
- pca
- k-means
- none of the above
Q8 | Which of the following model model include a backwards elimination feature selection routine?
- mcv
- mars
- mcrs
- all above
Q9 | Can we extract knowledge without apply feature selection
- yes
- no
Q10 | While using feature selection on the data, is the number of features decreases.
- no
- yes
Q11 | Which of the following are several models
- regression
- classification
- none of the above
Q12 | provides some built-in datasets that can be used for testing purposes.
- scikit-learn
- classification
- regression
- none of the above
Q13 | While using all labels are turned into sequential numbers.
- labelencoder class
- labelbinarizer class
- dictvectorizer
- featurehasher
Q14 | produce sparse matrices of real numbers that can be fed into any machine learning model.
- dictvectorizer
- featurehasher
- both a & b
- none of the mentioned
Q15 | scikit-learn offers the class , which is responsible for filling the holes using a strategy based on the mean, median, or frequency
- labelencoder
- labelbinarizer
- dictvectorizer
- imputer
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.
- minmaxscaler
- maxabsscaler
- both a & b
- none of the mentioned
Q17 | scikit-learn also provides a class for per- sample normalization,
- normalizer
- imputer
- classifier
- all above
Q18 | dataset with many features contains information proportional to the independence of all features and their variance.
- normalized
- unnormalized
- both a & b
- none of the mentioned
Q19 | In order to assess how much information is brought by each component, and the correlation among them, a useful tool is the .
- concuttent matrix
- convergance matrix
- supportive matrix
- covariance matrix
Q20 | The parameter can assume different values which determine how the data matrix is initially processed.
- run
- start
- init
- stop
Q21 | allows exploiting the natural sparsity of data while extracting principal components.
- sparsepca
- kernelpca
- svd
- init parameter
Q22 | Which of the following is true about Residuals ?
- lower is better
- higher is better
- a or b depend on the situation
- none of these
Q23 | Overfitting is more likely when you have huge amount of data to train?
- true
- false
Q24 | Which of the following statement is true about outliers in Linear regression?
- linear regression is sensitive to outliers
- linear regression is not sensitive to outliers
- cant say
- none of these