11 Inferential Statistics

05/10/20 • 16 min

We talk about the importance of inferential statistics in Data Science. Inferential statistics are a set of techniques used to make generalizations about a population from a sample. One of the tools used in inferential statistics is hypothesis testing. In this episode we provide a couple of examples on when and why to use 1-sample t-tests and 2-sample t-tests. We also argue that the mean or average of a sample means nothing if we do not also consider the variation of the data.

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10 Logistic Regression

Logistic regression is a very robust machine learning technique which can be used in three modes: binary, multinomial and ordinal. We talk about assumptions and some misconceptions. For example, people believe that because logistic regression fits only a linear separator in the expanded dimensional space it wouldn’t be able to fit a complex boundary in the original space. Also, people normally use either linear regression or multinomial logistic regression when they should be using ordinal logistic regression.

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12 Decision Trees

We talk about Decision Trees as one of the most basic statistical learning algorithms out there that all Data Scientist should know. Decision Trees are one of a few machine learning models which are easy to interpret which makes them a favorite when it is desired to understand the logic behind a certain decision. Decision Trees naturally handle all types of variables without the need to create dummy variables, no need to scale or normalize and they are also very robust against outliers.