Is there any resource with a list of feature engineering techniques? A mapping of type of data, model and feature engineering technique would be a gold mine.

I am having 'hour' field as my attribute, but it takes a cyclic values. How could I transform the feature to preserve the information like '23' and '0' hour are close not far. One way I could think is to do transformation: min(h, 23-h) Input: [0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23] Output: [0 1 2 3 4 5 6 7 8 9 …

I'm having trouble understanding the use of Vector in machine learning to represent a group of features. If one looks up the definition of a Vector, then, according to wikipedia, a Vector is an entity with a magnitude and direction. This can be understood when applying Vectors to for example physics to represent force, velocity, acceleration, etc...: the components of the Vector represent the components of the physical property along the axes in space. For example, the components of a …

I have IP addresses as feature and I would like to know how much two IP addresses are similar to each other to use the difference in an Euclidean distance measure (in order to quantify the similarities of my data points). What tactic can I use for this?

I am searching for pointers to algorithms for feature detection. EDIT: all the answers helped me a lot, I cannot decide which one I should accept. THX guys! What I did: For discrete variables (i.e. $D_i, E$ are finite sets) $X_i : \Omega \to D_i$ and a given data table $$ \begin{pmatrix}{} X_1 & ... & X_n & X_{n+1} \\ x_1^{(1)} & ... & x_n^{(1)} & x_{n+1}^{(1)} \\ ... \\ x_1^{(m)} & ... & x_n^{(m)} & x_{n+1}^{(m)} \\ \end{pmatrix} $$ …