Step-by-Step Guide to Logistic Regression for Beginners
Understanding Logistic Regression: A Comprehensive Introduction
In the world of data science and machine learning, logistic regression stands out as one of the most fundamental and widely used algorithms for classification tasks. Whether you’re predicting if an email is spam or not, diagnosing diseases, or even determining if a customer will purchase a product, logistic regression is a powerful tool in a data scientist’s arsenal.
What is Logistic Regression?
Logistic regression is a statistical method for analyzing datasets in which there are one or more independent variables that determine an outcome. The outcome is usually a binary variable (0 or 1, yes or no, true or false). Unlike linear regression, which predicts continuous outcomes, logistic regression is used for predicting categorical outcomes.
It is a Supervised ML algorithm.
Used for binary classification.
Great starter algorithm for text-related classification.
Improve by applying feature scaling.
Not much affected by outliers due to the sigmoid function tapers the outliers.
The output value is between 0 and 1.
Measure the accuracy of the model
Note: In logistic regression, we use different cost functions compared to linear regression due to having multiple local minima (non-convex function).
Loss function: measure error for single training example
cost function: It is the average of the loss function of the entire training set
Minimize the error
We use gradient descent algorithms to minimize cost function or the error between predicted and actual values in various machine learning algorithms.
Gradient descent is an optimization algorithm.
It updates the values of w and b.
Where W= weight and b=Bias.
Note: We need to perform feature scaling when dealing with gradient descent-based algorithms (linear regression, logistic regression, neural network) and distance-based algorithms (KNN, K-means, SVM) as they are sensitive to the range of the data points.
In the above equation, alpha determines the step size at each iteration while moving toward a minimum loss function and it is a tuning parameter in an optimization algorithm.
Pros
1.Simplicity and Interpretability
2.Efficiency
3.Regularization Techniques: Logistic regression can incorporate regularization techniques (such as L1 and L2 regularization) to prevent overfitting and handle multicollinearity.
4.Handling Multiclass Problems: Though inherently a binary classifier, logistic regression can be extended to multiclass classification problems using techniques like One-vs-Rest (OvR) or One-vs-One (OvO).
Cons
1. Assumption of Linearity: Logistic regression assumes a linear relationship between the independent variables and the log odds. It may not perform well if the true relationship is not linear.
2. Sensitivity to Outliers
3. Need for Feature Scaling
4. Overfitting with High Dimensionality.
