ML Logistic Regression.

  • Logistic regression is popular Machine Learning algorithms, which is part of Supervised Learning technique.
  • It is used for predicting the categorical dependent variables using a given set of independent variables. Where the model estimates the mathematical probability of whether an instance belongs to a specific category or not.
  • It does binary classification tasks by predicting the probability of an outcome. The Logistic regression model results binary output, which is limited to two possible outcomes i.e either yes or no, true/false or 0/1.
  • Logical regression analyzes the relationship between one or more independent variables and classifies data into discrete classes.
  • Some Real Life Examples -
    In Medical Field -
    • The Trauma and Injury Severity Score Prediction, which is widely used to predict mortality in injured patients.
    • Many other medical scales for determining the risk of developing a disease(e.g. diabetes; coronary heart disease) and to assess severity of a patient have been developed using logistic regression. Based on observed characteristics of the patient (age, sex, body mass index, blood tests etc.)
    In Politics
    • Predict for an election result whether a Congress Party voter will vote Congress or BJP Party in India or Any Other Party. Based on age, sex, trend, state of residence, votes in previous elections etc.
    In Engineering
    • Another example might be to The technique can also be used in engineering, especially for predicting the probability of failure of a given process, system or product.
    In Marketing
    • It is also used in marketing applications such as prediction of a customer's propensity to purchase a product or halt a subscription, etc.
    In Economics
    • It can be used to predict the likelihood of a person ending up in the labor force and a business application would be to predict the likelihood of a homeowner defaulting on a mortgage.
    Disaster Planning
    • Disaster planners and engineers rely on these models to predict decision take by householders or building occupants in small scale and large scales evacuations, such as building fires, wildfires, hurricanes among others. These models help in the development of reliable disaster managing plans and safer design for the built environment.