Machine Learning Interview Questions and Answers
Question - 41 : - How is machine learning used in day-to-day life?
Answer - 41 : -
Most of the people are already using machine learning in their everyday life. Assume that you are engaging with the internet, you are actually expressing your preferences, likes, dislikes through your searches. All these things are picked up by cookies coming on your computer, from this, the behavior of a user is evaluated. It helps to increase the progress of a user through the internet and provide similar suggestions.
The navigation system can also be considered as one of the examples where we are using machine learning to calculate a distance between two places using optimization techniques. Surely, people are going to more engage with machine learning in the near future.
Question - 42 : - Do you think that treating a categorical variable as a continuous variable would result in a better predictive model?
Answer - 42 : -
For a better predictive model, the categorical variable can be considered as a continuous variable only when the variable is ordinal in nature.
Question - 43 : - Why do we need to convert categorical variables into factor? Which functions are used to perform the conversion?
Answer - 43 : -
Most Machine learning algorithms require number as input. That is why we convert categorical values into factors to get numerical values. We also don't have to deal with dummy variables.
The functions factor() and as.factor() are used to convert variables into factors.
Question - 44 : - What is Regularization? What kind of problems does regularization solve?
Answer - 44 : -
A regularization is a form of regression, which constrains/ regularizes or shrinks the coefficient estimates towards zero. In other words, it discourages learning a more complex or flexible model to avoid the risk of overfitting. It reduces the variance of the model, without a substantial increase in its bias.
Regularization is used to address overfitting problems as it penalizes the loss function by adding a multiple of an L1 (LASSO) or an L2 (Ridge) norm of weights vector w.
Question - 45 : - When does regularization become necessary in Machine Learning?
Answer - 45 : -
Regularization is necessary whenever the model begins to overfit/ underfit. It is a cost term for bringing in more features with the objective function. Hence, it tries to push the coefficients for many variables to zero and reduce cost term. It helps to reduce model complexity so that the model can become better at predicting (generalizing).
Question - 46 : - What do you understand by Underfitting?
Answer - 46 : -
Underfitting is an issue when we have a low error in both the training set and the testing set. Few algorithms work better for interpretations but fail for better predictions.
Question - 47 : - What are the Recommended Systems?
Answer - 47 : -
Recommended System is a sub-directory of information filtering systems. It predicts the preferences or rankings offered by a user to a product. According to the preferences, it provides similar recommendations to a user. Recommendation systems are widely used in movies, news, research articles, products, social tips, music, etc.
Question - 48 : - How is a decision tree pruned?
Answer - 48 : -
Pruning is said to occur in decision trees when the branches which may consist of weak predictive power are removed to reduce the complexity of the model and increase the predictive accuracy of a decision tree model. Pruning can occur bottom-up and top-down, with approaches such as reduced error pruning and cost complexity pruning.
Reduced error pruning is the simplest version, and it replaces each node. If it is unable to decrease predictive accuracy, one should keep it pruned. But, it usually comes pretty close to an approach that would optimize for maximum accuracy.
Question - 49 : - What do you understand by the F1 score?
Answer - 49 : -
The F1 score represents the measurement of a model's performance. It is referred to as a weighted average of the precision and recall of a model. The results tending to 1 are considered as the best, and those tending to 0 are the worst. It could be used in classification tests, where true negatives don't matter much.
Question - 50 : - Why instance-based learning algorithm sometimes referred to as Lazy learning algorithm?
Answer - 50 : -
In machine learning, lazy learning can be described as a method where induction and generalization processes are delayed until classification is performed. Because of the same property, an instance-based learning algorithm is sometimes called lazy learning algorithm.