Kernel density estimation

term_id: kernel_density_estimation

Category: basic_concepts

Definition

Kernel Density Estimation (KDE) is a fundamental statistical technique that smooths discrete data points to create a continuous probability distribution curve. It places a kernel function, typically Gaussian, at each data point and sums them to estimate the underlying density. Unlike histograms, KDE does not depend on binning choices, providing a smoother and more accurate representation of data distribution. It is widely used in exploratory data analysis to understand feature distributions and detect anomalies.

Summary

A non-parametric method used to estimate the probability density function of a random variable based on a finite data sample.

Key Concepts

  • Probability Density Function
  • Non-parametric Statistics
  • Smoothing
  • Gaussian Kernel

Use Cases

  • Exploratory Data Analysis (EDA)
  • Anomaly detection in univariate data
  • Visualizing feature distributions in datasets

Code Example

1
2
3
4
5
6
7

from scipy.stats import gaussian_kde
import numpy as np

data = np.random.normal(0, 1, 100)
kde = gaussian_kde(data)
x_vals = np.linspace(-3, 3, 100)
y_vals = kde(x_vals)

Related