Logistic Regression is a statistical model used for classification tasks, particularly for binary classification problems where the target variable has two possible outcomes, such as success or failure. Here's a detailed explanation of this topic:
- Target Variable: In logistic regression, the target variable is binary, typically represented as 0 or 1.
- Logistic Function: The model uses a logistic function, also known as a sigmoid function, to transform linear outputs into probabilities ranging from 0 to 1. The formula is: [ \sigma(z) = \frac{1}{1 + e^{-z}} ] where ( z = \beta_0 + \beta_1 x_1 + \beta_2 x_2 + \ldots + \beta_n x_n ).
- Interpreting Probabilities: After obtaining probabilities from the logistic function, they can be converted into classifications (0 or 1) using a specified threshold (usually 0.5).
- Data Collection: Obtain a suitable dataset containing independent variables (features) and a binary target variable.
- Data Exploration: Analyze the data to understand its distribution and relationships between variables.
- Data Preparation:
- Handle missing values.
- Convert categorical variables into numerical features using encoding techniques (e.g., One-Hot Encoding).
- Split the data into training and testing sets.
- Model Training: Use the training data to train the logistic regression model.
- Prediction: Use the trained model to predict classifications on the test data.
- Performance Evaluation: Use metrics such as accuracy, confusion matrix, and classification report to evaluate the model's performance.