Advanced Database Management System - Tutorials and Notes: Machine Learning Exam Questions TRUE or FALSE 05

Wednesday, 13 May 2020

Machine Learning Exam Questions TRUE or FALSE 05

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


Machine Learning TRUE / FALSE Questions - SET 05

1. Given a linearly-separable dataset for a 2-class classification problem, a Linear SVM is better to use than a perceptron because the SVM will often be able to achieve better classification accuracy on the training set.

(a) TRUE                                                   (b) FALSE

View Answer

Answer: FALSE
False because both with have 100% accuracy on the linearly-separable training set.

2. Making a decision tree deeper will assure better fit but reduce robustness.

(a) TRUE                                                   (b) FALSE

View Answer

Answer: TRUE

Deeper tree increases the accuracy. The deeper the tree, the more complex the decision rules and the fitter the model.

Deeper trees are more complex and are more likely to overfit the training data. As the complexity increases the training error is reduced; but above a certain level of complexity, the test error also increases. This effect is called overfitting. Due to overfitting, the model may perform well for training data and not for unseen data.

3. Using a model with less bias is always better than using a model with more bias.

(a) TRUE                                                   (b) FALSE

View Answer

Answer: TRUE

Bias is the difference between your model's expected predictions and the true values.

Low bias (high variance) algorithms tend to be more complex, with flexible underlying structure. They train models that are accurate on average, but inconsistent.
Model with high bias pays very little attention to the training data and oversimplifies the model. It always leads to high error on training and test data. Hence, it is good to use a model with less bias.

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