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3 Machine Learning Quiz Questions with Answers explanation, Interview
questions on machine learning, quiz questions for data scientist answers
explained, machine learning exam questions, question bank in machine
learning, k-nearest neighbor, decision tree, linear regression
Machine learning Quiz Questions - Set 31
1. Which of the following machine learning algorithms has both training and test phases?
a) k-Nearest Neighbor
b) Linear regression
c) Case-based reasoning
d) None of the above
Answer: (b) Linear regression Linear regression is one of the most commonly used predictive modeling techniques. Regression models are supervised learning models that are generally used when the value to be predicted is of discrete or quantitative nature. The task of linear regression model is to obtain a line that best fits the data. In linear regression, we train the model on training data set to find the linear equation. |
2. Given a kNN classifier, which one of the following statements is true?
a) The more examples are used for classifying an example, the higher accuracy we obtain
b) The more attributes we use to describe the examples the more difficult is to obtain high accuracy
c) The most costly part of this method is to learn the model
d) We can use kNN for classification only
Answer: (b) The more attributes we use to describe the examples the more difficult is to obtain high accuracy kNN becomes significantly slower as the number of examples (independent variables) increases. When the number of features increases, then it requires more data. When there’s more data, it creates an overfitting problem because no one knows which piece of noise will contribute to the model. kNN performs better with low dimensions (low number of features). For more, you can refer here. https://neptune.ai/blog/knn-algorithm-explanation-opportunities-limitations |
3. Decision trees can work with
a) Only numeric values
b) Only nominal values
c) Both numeric and nominal values
d) Neither numeric nor nominal values
Answer: (c) Both numeric and nominal values Decision trees can handle both numerical and categorical data. Early decision trees were only capable of handling categorical variables, but more recent versions, such as C4.5, CART do not have this limitation. The categorical data are encoded, if required (eg. one-hot encoding), and used by decision tree algorithms. |