Data Science Simplified: Key Concepts of Statistical Learning
In the first article of this series, I had touched upon key concepts and processes of Data Science. In this article, I will dive in a bit deeper. First, I will define what is Statistical learning. Then, we will dive into key concepts in Statistical learning. Believe me; it is simple.
As per Wikipedia, Statistical learning theory is a framework for machine learning drawing from the fields of statistics and functional analysis.
Machine learning is a manifestation of statistical learning techniques implemented through software applications.
What does this mean in practice? Statistical learning refers to tools and techniques that enable us to understand data better. Let’s take a step back here. What do we mean by understanding the data?
In the context of Statistical learning, there are two types of data:
The data that can’t be controlled i.e. dependent variables need to predicted or estimated.
Understanding the data better is to figure out more about the dependent variable in terms of independent variables. Let me illustrate it with an example:
Say that I want to measure sales based on the advertising budget I allocate for TV, Radio, and Print. I can control the budget that I can assign to TV, Radio, and Print. What I cannot control is how they will impact the sales. I want to express data that I cannot control (sales) as a function of data that I can control (advertising budget). I want to uncover this hidden relationship.
Statistical learning reveals hidden data relationships. Relationships between the dependent and the independent data.
One of the famous business models in operations management is the ITO model. It stands for Input-Transformation-Output model. It is simple. There are inputs. These inputs undergo some transformations. An output is created.
Statistical learning also applies a similar concept. There are input data. Input data is transformed. The output, something that needs to be predicted or estimated, is generated.
The transformation engine is called as a Model. These are functions that estimate the output.
The transformation is mathematical. Mathematical ingredients are added to the input data to estimate the output. These ingredients are called as the Parameters.
Let’s walk through an example:
What determines someone’s income? Say that income is a determined by one’s year’s education and years of experience. A model that estimate is the income can be something like this:
β0 and β1 are parameters that express income as a function of education and experience.
Education and experience are controllable variables. These controllable variables have different synonyms. They are called as dependent variables. They are also called as features.
Income is uncontrollable variable. They are called as targets.
What do we do when we have to prepare for an examination? Study. Learn. Imbibe. Take notes. Practice mock papers. These are the tools to learn and to prepare for the unseen test.
