Logistic Regression

Machine Learning Practice

Logistic Regression (also called Logit Regression) is commonly used to estimate the probability that an instance belongs to a particular class.

Introduction

If the estimated probability is greater than 50%, then the model predicts that the instance belongs to that class (called the positive class, labeled “1”), or else it predicts that it does not (i.e., it belongs to the negative class, labeled “0”).

Cleveland Heart-disease dataset

Attribute Information:

Age (in years)
Sex (1 = male; 0 = female)
cp -chest pain type
trestbps - resting blood pressure (anything above 130-140 is typically cause for concern)
chol-serum cholestoral in mg/dl (above 200 is cause for concern)
restecg - resting electrocardiographic results (0 = normal;1 = having ST-T wave abnormality; 2 = showing probable or definite left ventricular hypertrophy by Estes' criteria)
thalach-maximum heart rate achieved

Cleveland Heart-disease dataset

Attribute Information:

exang - exercise induced angina (1 = yes; 0 = no)
oldpeak - depression induced by exercise relative to rest
slope - slope of the peak exercise ST segment (1 = upsloping; 2 = flat Value; 3 = downsloping)
ca - number of major vessels (0-3) colored by flourosopy
thal - (3 = normal; 6 = fixed defect; 7 = reversable defect
num (target) - diagnosis of heart disease (angiographic disease status)( 0: < 50% diameter narrowing ; 1: > 50% diameter narrowing)