Monday, 29 June 2020

Machine Learning Exam Questions TRUE or FALSE 14

Machine learning quiz questions TRUE or FALSE with answers, important machine learning interview questions for data science, Top 3 machine learning question set, ML exam questions

Machine Learning TRUE / FALSE Questions - SET 14

1. Logistic regression is equivalent to a neural network without hidden units and using cross-entropy loss.

(a) TRUE                                                   (b) FALSE


2. Less training data is one of the benefits of the parametric learning algorithm.

(a) TRUE                                                   (b) FALSE


3. K-nearest neighbor is a parametric learning algorithm.

(a) TRUE                                                   (b) FALSE


4. Convolutional neural networks generally have fewer free parameters as compared to fully connected neural networks.

(a) TRUE                                                   (b) FALSE


5. The Linear Discriminant Analysis (LDA) classifier computes the direction maximizing the ratio of between-class variance over within-class variance.

(a) TRUE                                                   (b) FALSE


6. A good way to pick the number of clusters, k, used for k-Means clustering is to try multiple values of k and choose the value that minimizes the distortion measure.

(a) TRUE                                                   (b) FALSE


7. PCA can be kernelized.

(a) TRUE                                                   (b) FALSE


8. Grid search is less prone to being trapped in a local minimum than other heuristic search methods.

(a) TRUE                                                   (b) FALSE


9. CNNs can learn to recognize an object in an image no matter how the object is translated (i.e., shifted horizontally and/or vertically) even if the training set only includes that object in one position.

(a) TRUE                                                   (b) FALSE


10. As the value of k used in a k-NN classifier is incrementally increased from 1 to n, the total number of training examples, the classification accuracy on the training set will always increase.

(a) TRUE                                                   (b) FALSE



View Answer


2) TRUE - They do not require as much training data and can work well even if the fit to the data is not perfect.

3) FALSE – Knn is a non-parametric learning algorithm since the number of parameters grows with the size of the training set.



6) FALSE - there is no single best way to determine K from the data

7) TRUE – PCA works well for linearly separable data. It does not perform well for non-linear data. Kernelized PCA is useful in dimensionality reduction of non-linear data.


9) TRUE – because of shared weights

10) FALSE - The training set accuracy when k=1 will be 100%. As k approaches the total number of training examples more and more examples influence the class, and eventually the class will always be the majority class in the training set.


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