Logistic Regression: A Comprehensive Guide for Beginners - Prof. Verma, Summaries of Machine Learning

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LOGISTICS

REGRESSION

By : Kartik 22BCA

Lavish 22BCA

What is logistic regression? ● Logistic regression estimates the probability of an event occurring, such as voted or didn’t vote, based on a given data set of independent variables. ● Also known as logit model. ● Often used for classification and predictive analytics. Since the outcome is a probability, the dependent variable is bounded between 0 and 1. ● The logistic function is defined mathematically as σ(t) = 1 / (1 + e^(-z)).

where z=wTX+bz = w^T X + bz=wTX+b is a linear combination of the input features.

Types of logistic regression There are three types of logistic regression models Binary logistic regression : In this approach, the response or dependent variable is dichotomous in nature—i.e. it has only two possible outcomes (e.g. 0 or 1). Multinomial logistic regression : In this type of logistic regression model, the dependent variable has three or more possible outcomes; however, these values have no specified order. Ordinal logistic regression : This type of logistic regression model is leveraged when the response variable has three or more possible outcome, but in this case, these values do have a defined order.

Use cases of logistic regression Fraud detection : Logistic regression models can help teams identify data anomalies, which are predictive of fraud. Certain behaviors or characteristics may have a higher association with fraudulent activities, which is particularly helpful to banking and other financial institutions in protecting their clients. Disease prediction : In medicine, this analytics approach can be used to predict the likelihood of disease or illness for a given population. Healthcare organizations can set up preventative care for individuals that show higher propensity for specific illnesses.

Advantages ● (^) Simple to implement and interpret. ● (^) Computationally efficient for small to medium datasets. ● (^) Provides probabilistic outputs, useful for decision-making. ● (^) Incorporating regularization helps manage multicollinearity, where features are highly correlated.

Disadvantages ● (^) Assumes linearity between features and log-odds. ● (^) Sensitive to outliers. ● (^) Struggles with highly imbalanced datasets. ● (^) Does not perform well with complex, non-linear relationships.