Unsupervised Learning?If an algorithm learns something from the training data so that the knowledgecan be applied to the test data, then it is referred to as Supervised Learning.Classification is an example for Supervised Learning. If the algorithm does notlearn anything beforehand because there is no response variable or any trainingdata, then it is referred to as unsupervised learning. Clustering is an example forunsupervised learning.

It is a statistical hypothesis testing for randomized experiment with two variablesA and B. The goal of A/B Testing is to identify any changes to the web page tomaximize or increase the outcome of an interest. An example for this could beidentifying the click through rate for a banner ad.

Eigenvectors are used for understanding linear transformations. In data analysis,we usually calculate the eigenvectors for a correlation or covariance matrix.Eigenvectors are the directions along which a particular linear transformationacts by flipping, compressing or stretching. Eigenvalue can be referred to as thestrength of the transformation in the direction of eigenvector or the factor bywhich the compression occurs.

There are various methods to assess the results of a logistic regression analysis-

• Using Classification Matrix to look at the true negatives and falsepositives.
• Concordance that helps identify the ability of the logistic model todifferentiate between the event happening and not happening.
• Lift helps assess the logistic model by comparing it with randomselection.