Rapid Fire on Machine Learning

Error is a sum of bias error+variance error+ irreducible error in regression. Bias and variance error can be reduced but not the irreducible error.   In regression, the error term (also known as the residual or residual error) represents the difference between the observed values of the dependent variable and the values predicted by the … Read more

A test result which wrongly indicates that a particular condition or attribute is absent. Example – “it’s possible to have a false negative—the test says you aren’t pregnant when you are”   In the context of machine learning, a false negative occurs when a model incorrectly predicts a negative outcome (i.e., classifies an instance as … Read more

It is a test result which wrongly indicates that a particular condition or attribute is present. Example – “Stress testing, a routine diagnostic tool used in detecting heart disease, results in a significant number of false positives in women”   In the context of machine learning, a false positive occurs when a model incorrectly predicts … Read more

Amazon uses a collaborative filtering algorithm for the recommendation of similar items. It’s a user to user similarity based mapping of user likeness and susceptibility to buy.   Amazon primarily uses a combination of collaborative filtering and content-based filtering for its recommendation system. Collaborative filtering involves recommending items based on the preferences and behavior of … Read more

It is the number of independent values or quantities which can be assigned to a statistical distribution. It is used in Hypothesis testing and chi-square test.   In the context of machine learning, the term “degrees of freedom” typically refers to the number of values in the final calculation of a statistic that are free … Read more