Machine Learning Interview Questions and Answers
Machine Learning interviews require extensive preparation because candidates are assessed on technical and programming skills, ML knowledge, and more. Aspiring Machine Learning professionals must know what interview questions hiring managers may ask.
These essential ML questions have been narrowed to simplify your learning journey. These questions will help you get jobs as a Machine Learning Engineer, Data Scientist, Computational Linguist, Software Developer, BI Developer, NLP Scientist, and more.
Machine Learning Interview Questions
- Water Trapping Problem
- What are the advantages and disadvantages of using an Array?
- Explain Eigenvectors and Eigenvalues.
- What is Heteroscedasticity?
- What are the hyperparameters of a logistic regression model?
- What is a voting model?
- What distance metrics can be used in KNN?
- Which algorithms can be used for important variable selection?
- What is the role of maximum likelihood in logistic regression.
- Which type of sampling is better for a classification model and why?
- If we have a high bias error what does it mean? How to treat it?
- What is a false positive?
- What do you mean by the ROC curve?
- Which performance metric is better R2 or adjusted R2?
- What is the 68 per cent rule in normal distribution?
- What is the degree of freedom?
- What are the advantages of SVM algorithms?
- What is log likelihood in logistic regression?
- What are the benefits of pruning?
- What is Pandas Profiling?
- How is PCA different from LDA?
- Which algorithm can be used in value imputation in both categorical and continuous categories of data?
- What is a good metric for measuring the level of multicollinearity?
- What is a pipeline?
- What is normal distribution?
- What is a random variable?
- When can be a categorical value treated as a continuous variable and what effect does it have when done so?
- What ensemble technique is used by Random forests?
- What impact does correlation have on PCA?
- How to deal with very few data samples? Is it possible to make a model out of it?
- How do you deal with the class imbalance in a classification problem?
- Which distance do we measure in the case of KNN?
- Which kind of recommendation system is used by amazon to recommend similar items?
- What is Lists in Python?
- How is p-value useful?
- How to deal with multicollinearity?
- Can logistic regression be used for classes more than 2?
- How would you define the number of clusters in a clustering algorithm?
- Given a string S consisting only ‘a’s and ‘b’s, print the last index of the ‘b’ present in it.
- What is an Array?
- Which metrics can be used to measure correlation of categorical data?
- What are the hyperparameters of an SVM?
- Name a few hyper-parameters of decision trees?
- What are the performance metrics that can be used to estimate the efficiency of a linear regression model?
- What is the default method of splitting in decision trees?
- Rotate the elements of an array by d positions to the left. Let us initially look at an example.
- What is the role of cross-validation?
- Is ARIMA model a good fit for every time series problem?
- When should ridge regression be preferred over lasso?
- What ensemble technique is used by gradient boosting trees?
- Which sampling technique is most suitable when working with time-series data?
- What is a chi-square test?
- What is a false negative?
- What do you mean by AUC curve?
- What’s the difference between Type I and Type II error?
- What do you understand by selection bias in Machine Learning?
- What is Kernel SVM?
- What are the advantages of using a naive Bayes for classification?
- Which one is better, Naive Bayes Algorithm or Decision Trees?
- What Are the Three Stages of Building a Model in Machine Learning?
- What is the difference between the normal soft margin SVM and SVM with a linear kernel?
- How would you evaluate a logistic regression model?
- What is the difference between Entropy and Information Gain?
- What is the difference between the Naive Bayes Classifier and the Bayes classifier?
- Why does XGBoost perform better than SVM?
- What do you understand by L1 and L2 regularization?
- What is the error term composed of in regression?
- What is the difference between SVM Rank and SVR (Support Vector Regression)?
- Is naive Bayes supervised or unsupervised?
- Are Gaussian Naive Bayes the same as binomial Naive Bayes?
- What are collinearity and multicollinearity?
- What is the process of carrying out a linear regression?
- In what real world applications is Naive Bayes classifier used?
- How Do You Design an Email Spam Filter in Machine Learning?
- What do you understand by Precision and Recall?
- How is linear classifier relevant to SVM?