Understanding Classification Algorithms

Table of Contents

• What is Classification?
• Logistic Regression
• K‑Nearest Neighbours
• Support Vector Machines
• Decision Trees & Random Forests
• Naïve Bayes
• Choosing the Right Model
• Conclusion

What is Classification?

Classification is a type of supervised learning where the goal is to assign a categorical label to new observations based on patterns learned from a labeled training set. Typical examples include spam detection, image recognition, and medical diagnosis.

“A model that predicts the class of an input better than random chance is considered a classifier.” – Machine Learning Handbook

Logistic Regression

Despite its name, logistic regression is primarily used for binary classification. It models the probability that an instance belongs to a particular class using the logistic function:

p(y=1|x) = 1 / (1 + e- (β₀ + β₁x₁ + … + βₙxₙ))

Key points:

• Linear decision boundary in the feature space.
• Easy to interpret coefficients as odds ratios.
• Works well when classes are linearly separable.

K‑Nearest Neighbours (KNN)

KNN is a non‑parametric, instance‑based learning algorithm. Classification is performed by looking at the k closest training samples (according to a distance metric) and taking a majority vote.

Pros:

• No training phase—instant model creation.
• Can capture complex decision boundaries.

Cons:

• Computationally expensive at prediction time.
• Sensitive to irrelevant features and the choice of k.

Support Vector Machines (SVM)

SVM aims to find the hyperplane that maximizes the margin between classes. With kernels, it can transform data into higher dimensions to handle non‑linear separations.

max  γ
s.t. yᵢ (w·φ(xᵢ) + b) ≥ γ,  i = 1…m

Popular kernels: linear, polynomial, radial basis function (RBF).

Decision Trees & Random Forests

Decision trees split the feature space recursively based on impurity measures (e.g., Gini, entropy). They are easy to visualize and interpret.

Random Forests combine many trees trained on bootstrapped subsets of data and random feature subsets, reducing over‑fitting and improving accuracy.

• Pros: Handles mixed data types, robust to outliers.
• Cons: Individual trees can be unstable; forests lose interpretability.

Naïve Bayes

Based on Bayes’ theorem, this family assumes feature independence given the class label:

P(y|x) ∝ P(y) ∏ P(xᵢ|y)

Works surprisingly well for text classification (e.g., spam filtering) despite the strong independence assumption.

Choosing the Right Model

Consider the following factors when selecting a classifier:

Conclusion

Classification algorithms each have unique strengths and trade‑offs. A practical workflow typically starts with data exploration, baseline modeling (often logistic regression or Naïve Bayes), and then iteratively tries more complex models such as SVMs or ensemble methods. Always validate with cross‑validation and monitor metrics like accuracy, precision, recall, and ROC‑AUC.

Happy modeling! 🚀