Dataset For Logistic Regression

This dataset originates from the MAGIC Gamma Telescope project. It is designed for the classification of high-energy gamma-ray signals and background hadrons. Each row corresponds to a signal or background observation, and the dataset includes 11 features describing the characteristics of each observation.

Features:
fLength: Major axis of the ellipse, measured in millimeters (continuous).
fWidth: Minor axis of the ellipse, measured in millimeters (continuous).
fSize: Logarithmic sum of the content of all pixels in the telescope's image (continuous).
fConc: Ratio of the sum of the two highest pixel values over the total size (continuous).
fConc1: Ratio of the highest pixel value over the total size (continuous).
fAsym: Distance from the highest pixel to the center of the ellipse, projected onto the major axis (continuous).
fM3Long: 3rd root of the third moment along the major axis, in millimeters (continuous).
fM3Trans: 3rd root of the third moment along the minor axis, in millimeters (continuous).
fAlpha: Angle of the major axis relative to the vector to the origin, in degrees (continuous).
fDist: Distance from the origin to the center of the ellipse, in millimeters (continuous).
class: Target variable, representing:
g: Gamma-ray signal (positive class).
h: Hadron noise (negative class).
Applications:
This dataset is ideal for:

Binary classification tasks.
Model benchmarking for imbalanced datasets.
Feature importance analysis in gamma-ray astronomy.
Source:
The data has been synthetically generated based on the characteristics observed in the MAGIC Gamma Telescope. It serves as an excellent resource for machine learning and astronomy-related studies.