Frequently Asked Questions(FAQs)

1.What is the purpose of KDE plot?

The KDE plot visually represents the probability density of a continuous variable, offering insights into the data’s distribution, shape, and central tendency.

2.What is the use of KDE in Python?

In Python, KDE (Kernel Density Estimation) is used for efficient visualization of probability density functions, especially in statistical libraries like Seaborn and Matplotlib.

3.What is the difference between histogram and KDE plot?

While histograms display data distribution through bins, KDE plots use a smooth curve to estimate probability density, providing a continuous and visually refined representation of the underlying distribution.

4.What does a kernel density plot show?

A kernel density plot shows the smoothed probability density of a dataset. It highlights peaks, modes, and trends, aiding in the visual exploration of continuous variable distributions.

Kernel Density Estimate (KDE) plot, a visualization technique that offers a detailed view of the probability density of continuous variables. In this article, we will be using Iris Dataset and KDE Plot to visualize the insights of the dataset.