Causal modeling is aimed at advancing reasonable hypotheses about underlying causal relationships between the dependent and independent variables. Consider for example a simple linear model: y = a0 + a1 x1 + a2 x2 + e where y is the dependent variable, x1 and x2 are independent variables, e is the contribution of all otherContinue reading “Week #2 – Casual Modeling”
Monthly Archives: February 2016
Week #10 – Arm
In an experiment, an arm is a treatment protocol – for example, drug A, or placebo. In medical trials, an arm corresponds to a patient group receiving a specified therapy. The term is also relevant for bandit algorithms for web testing, where an arm consists of a specific web treatment or offer. Assigning a webContinue reading “Week #10 – Arm”
Week #9 – Sparse Matrix
A sparse matrix typically refers to a very large matrix of variables (features) and records (cases) in which most cells are empty or 0-valued. An example might be a binary matrix used to power web searches – columns representing search terms and rows representing searches, and cells populated by 1’s or 0’s (presence or absenceContinue reading “Week #9 – Sparse Matrix”
Week #8 – Homonyms department: Sample
We continue our effort to shed light on potentially confusing usage of terms in the different data science communities. In statistics, a sample is a collection of observations or records. It is often, but not always, randomly drawn. In matrix form, the rows are records (subjects), columns are variables, and cell values are the valuesContinue reading “Week #8 – Homonyms department: Sample”
Week #7 – Homonyms department: Normalization
With this entry, we inaugurate a new effort to shed light on potentially confusing usage of terms in the different data science communities. In statistics and machine learning, normalization of variables means to subtract the mean and divide by the standard deviation. When there are multiple variables in an analysis, normalization (also called standardization) removesContinue reading “Week #7 – Homonyms department: Normalization”