FAQ on Machine Learning

Rotation in PCA is very important as it maximizes the separation within the variance obtained by all the components because of which interpretation of components would become easier. If the components are not rotated, then we need extended components to describe variance of the components. In Principal Component Analysis (PCA), rotation of components is important … Read more

The idea here is to reduce the dimensionality of the data set by reducing the number of variables that are correlated with each other. Although the variation needs to be retained to the maximum extent. The variables are transformed into a new set of variables that are known as Principal Components’. These PCs are the … Read more

The Curse of Dimensionality refers to the situation when your data has too many features. The phrase is used to express the difficulty of using brute force or grid search to optimize a function with too many inputs. It can also refer to several other issues like: If we have more features than observations, we … Read more

Marginalisation is summing the probability of a random variable X given joint probability distribution of X with other variables. It is an application of the law of total probability. P(X=x) = ∑YP(X=x,Y) Given the joint probability P(X=x,Y), we can use marginalization to find P(X=x). So, it is to find distribution of one random variable by … Read more

Associative Rule Mining is one of the techniques to discover patterns in data like features (dimensions) which occur together and features (dimensions) which are correlated. It is mostly used in Market-based Analysis to find how frequently an itemset occurs in a transaction. Association rules have to satisfy minimum support and minimum confidence at the very … Read more