Machine Learning Interview Questions

When does regularization becomes necessary in Machine Learning? Regularization becomes necessary when the model begins to ovefit / underfit. This technique introduces 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 hence reduce cost term. This helps to reduce … Read more

What is the difference between supervised and unsupervised machine learning? Supervised learning requires training labeled data. For example, in order to do classification (a supervised learning task), you’ll need to first label the data you’ll use to train the model to classify data into your labeled groups. Unsupervised learning, in contrast, does not require labeling … Read more

Differentiate between Boosting and Bagging? Bagging and Boosting are variants of Ensemble Techniques. Bootstrap Aggregation or bagging is a method that is used to reduce the variance for algorithms having very high variance. Decision trees are a particular family of classifiers which are susceptible to having high bias. Decision trees have a lot of sensitiveness to … Read more

What ensemble technique is used by Random forests? Bagging is the technique used by Random Forests. Random forests are a collection of trees which work on sampled data from the original dataset with the final prediction being a voted average of all trees. Both being tree-based algorithms, how is Random Forest different from Gradient Boosting … Read more

What is Kernel SVM? Kernel SVM is the abbreviated version of the kernel support vector machine. Kernel methods are a class of algorithms for pattern analysis, and the most common one is the kernel SVM. What are 3 data preprocessing techniques to handle outliers? Winsorize (cap at threshold). Transform to reduce skew (using Box-Cox or … Read more