Machine Learning in Simple Words: Statistical Significance Tests

Developing Intuition: “Statistically Significant” or “By Chance”?

Statistical tests are used to determine how likely are observed relationships between two variables or groups NOT a result of some random guess, luck, erratic fluctuations, noise or sampling error, but due to the fact that they really are related to one another. It is a mathematical way of stating if we have enough evidence to reject a ‘thought’(or in statistical jargon a ‘Hypothesis’) that a certain relationship between two variables just happened by chance.

Figure Source: Agresti and Franklin, Statistics: The Art and Science of Learning from Data (p. 468)

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Machine Learning in Simple Words: Decision Tree Series

Decision trees are one of the most widely used machine learning algorithms used today. In this part, let us learn at a very high level, how they work.

PART 1: A visual guide towards their working

Example Outline:

Suppose we are trying to find out whether a patient is a positive case of a certain disease, using the length and width of the suspect virus found in his/her blood. We obtained test results of 16 patients. Since decision tree is a form of ‘supervised learning’, we will first have to train the decision tree model by providing it with the sets of virus lengths and widths that resulted in positive casesand the ones which did not, before it is able to do predictions on its own. Upon plotting this data obtained from each patient, let us assume we landed up with a graph shown below:

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