Hexaware Machine Learning Interview Questions

Hexaware – Machine Learning Interview Questions

Here is the list Machine Learning Interview Questions which are recently asked in Hexaware company. These questions are included for both Freshers and Experienced professionals.

1. What is classifier in machine learning?

A classifier in machine learning is an algorithm that automatically orders or categorizes data into one or more of a set of “classes.” One of the most common examples is an email classifier that scans emails to filter them by class label: Spam or Not Spam.

2. What are the advantages of Naive Bayes?

It handles both continuous and discrete data. It is highly scalable with the number of predictors and data points. It is fast and can be used to make real-time predictions. It is not sensitive to the irrelevant features.

3. In what areas Pattern Recognition is used?

Pattern recognition is the automated recognition of patterns and regularities in the data. It has applications in statistical data analysis, signal processing, image analysis, information retrieval, bioinformatics, data compression, computer graphics and machine learning.

4. What is Genetic Programming?

Genetic programming is a domain-independent method that genetically breeds a population of computer programs to solve a problem. Specifically, genetic programming iteratively transforms a population of computer programs into a new generation of programs by applying analogs of the naturally occurring genetic operations.

5. What is Inductive Logic Programming in Machine Learning?

Inductive logic programming is the subfield of machine learning that the uses first-order logic to represent hypotheses and data. Because first-order logic is expressive and declarative, inductive logic programming specifically targets problems involving structured data and background knowledge.

6. What is Model Selection in Machine Learning?

Model selection is the process of selecting one final machine learning model from among a collection of candidate machine learning models for a training dataset. Model selection is the process of choosing the one of the models as the final model that addresses the problem.

7. What are the two methods used for the calibration in Supervised Learning?

There are two types of Supervised Learning techniques: Regression and Classification. Classification separates the data, Regression fits the data.

8. Which method is frequently used to prevent overfitting?

Regularization methods are so widely used to reduce overfitting that the term “regularization” may be used for any method that the improves the generalization error of a neural network model.

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9. What is the difference between heuristic for rule learning and heuristics for decision trees?

Heuristic for rule learning and heuristics for decision trees. The difference is that the heuristics for decision trees evaluate the average quality of a number of disjointed sets while rule learners only evaluate the quality of the set of instances that is covered with the candidate rule.

10. What is Perceptron in Machine Learning?

The perceptron is an algorithm for supervised learning of binary classifiers. It is a type of linear classifier, a classification algorithm that makes its predictions based on a linear predictor function combining a set of the weights with the feature vector.

11. Explain the two components of Bayesian logic program?

The first component is the logical one. It consists of a set of Bayesian clauses which captures the qualitative structure of the domain and is based on the"pure" Prolog. The second component is the quan- titative one.

12. What are Bayesian Networks (BN) ?

Bayesian networks are a type of Probabilistic Graphical Model that can be used to the build models from data or expert opinion. They can be used for a wide range of tasks including prediction, anomaly detection, diagnostics, automated insight, reasoning, time series prediction and decision making under uncertainty.

13. Why instance based learning algorithm sometimes referred as Lazy learning algorithm?

Instance-based methods are sometimes referred to as the lazy learning methods because they delay processing until a new instance must be classified. The nearest neighbors of an instance are defined in the terms of Euclidean distance.

14. What are the two classification methods that SVM ( Support Vector Machine) can handle?

Linear SVM is used for the linearly separable data, which means if a dataset can be classified into two classes by using a single straight line, then such data is termed as linearly separable data, and classifier is used called as Linear SVM classifier.

15. What is ensemble learning?

An ensemble is a machine learning model that the combines the predictions from two or more models. The models that contribute to the ensemble, referred to as ensemble members, may be the same type or different types and may or may not be trained on the same training data.

16. Why ensemble learning is used?

Ensemble learning is the process by which the multiple models, such as classifiers or experts, are strategically generated and combined to solve a particular computational intelligence problem. Ensemble learning is primarily used to improve the classification, prediction, function approximation, etc.

TOP MNC's MACHINE LEARNING INTERVIEW QUESTIONS & ANSWERS

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