Rapid Fire on Machine Learning

Popular dimensionality reduction algorithms are Principal Component Analysis and Factor Analysis. Principal Component Analysis creates one or more index variables from a larger set of measured variables. Factor Analysis is a model of the measurement of a latent variable. This latent variable cannot be measured with a single variable and is seen through a relationship … Read more

Yes, it is possible to test for the probability of improving model accuracy without cross-validation techniques. We can do so by running the ML model for say n number of iterations, recording the accuracy. Plot all the accuracies and remove the 5% of low probability values. Measure the left [low] cut off and right [high] … Read more

There are mainly six types of cross validation techniques. They are as follow: K fold Stratified k fold Leave one out Bootstrapping Random search cv Grid search cv In a machine learning interview, when asked about popular cross-validation techniques, you should mention several commonly used methods. Cross-validation is essential for assessing the performance and generalization … Read more

Outlier is an observation in the data set that is far away from other observations in the data set. We can discover outliers using tools and functions like box plot, scatter plot, Z-Score, IQR score etc. and then handle them based on the visualization we have got. To handle outliers, we can cap at some … Read more

Boosting focuses on errors found in previous iterations until they become obsolete. Whereas in bagging there is no corrective loop. This is why boosting is a more stable algorithm compared to other ensemble algorithms. Boosting is often considered a more stable algorithm compared to other ensemble methods due to several reasons: Sequential Learning: Boosting algorithms … Read more