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Step-by-Step Guide
Understand Probability Distributions: Find the Best Distribution for Your Data.
Knowing the underlying data distribution is crucial. This opens up numerous applications, including statistical inference, anomaly detection, synthetic data, and data compression.
Determining the underlying (probability) distribution for your data has great benefits when working in the field of data science. It is not only to better understand, explore, and prepare the data, but also to bring fast and lightweight solutions. Examples include decision-making, statistical inferences, synthetic data generation, and predictive modeling, among others. In general, the easiest manner to determine the underlying distribution is by visually inspecting the random variable(s) using a histogram. However, to determine the exact distribution with its parameters (e.g., loc, scale), it is essential to use quantitative methods.
In this blog, I will describe why it is important to determine the underlying probability distribution for your data set. But also explain the differences between parametric and non-parametric distributions, together with univariate and multivariate distributions. In the second part are hands-on examples to…
