Essential Machine Learning Algorithms: From Linear Regression to DBSCAN

In recent years, the demand for technology has driven the rapid adoption of machine learning, which creates value from data across many industries. Most ML products are built using off‑the‑shelf algorithms that often require slight adjustments.

Machine‑learning algorithms can be divided into three major categories: supervised learning, unsupervised learning, and reinforcement learning.

1. Linear Regression

Linear regression is a supervised algorithm that fits a linear equation to model the relationship between a continuous target variable and one or more independent variables. A positive correlation can be visualized with a scatter plot, and the ordinary least‑squares method finds the line that minimizes the squared distance to the data points.

2. Support Vector Machine (SVM)

SVM is a supervised algorithm mainly used for classification (and also regression). It constructs a decision boundary that maximizes the margin to the support vectors. When data are not linearly separable, kernel tricks map them to a higher‑dimensional space without explicit transformation.

3. Naive Bayes

Naive Bayes is a supervised classifier that applies Bayes’ theorem under the (naïve) assumption that features are independent. It computes class probabilities from feature likelihoods, which can be estimated directly from the data.

4. Logistic Regression

Logistic regression is a supervised algorithm for binary classification. It uses the sigmoid (logistic) function to map any real‑valued input to a probability between 0 and 1, and a threshold (commonly 0.5) determines the predicted class.

5. k‑Nearest Neighbors (kNN)

kNN is a simple supervised method for classification and regression that assigns a label based on the majority vote of the k closest data points. The choice of k balances over‑fitting (small k) and under‑fitting (large k).

6. Decision Tree

Decision trees recursively partition the data by asking questions that increase node purity. They are easy to visualize but can easily overfit; limiting tree depth mitigates this.

7. Random Forest

Random forest builds an ensemble of decision trees using bootstrap sampling and random feature selection. The majority vote (classification) or average (regression) of the trees yields a model that is more accurate and less prone to overfitting than a single tree.

8. Gradient Boosting Decision Tree (GBDT)

GBDT is an ensemble method that adds trees sequentially, each one correcting the errors of the previous ensemble. Learning rate and the number of estimators are key hyper‑parameters; too many trees can cause overfitting.

9. k‑Means Clustering

k‑Means is an unsupervised algorithm that partitions data into k clusters by iteratively assigning points to the nearest centroid and recomputing centroids until convergence.

10. Hierarchical Clustering

Hierarchical clustering builds a tree of clusters either by agglomeratively merging nearest clusters or divisively splitting a large cluster. The dendrogram visualizes the hierarchy, and the process can stop based on a desired number of clusters or a distance threshold.

11. DBSCAN

DBSCAN groups points that are densely packed, labeling points as core, border, or noise based on two parameters: eps (neighborhood radius) and minPts (minimum points). It can discover arbitrarily shaped clusters and is robust to outliers.

12. Principal Component Analysis (PCA)

PCA is an unsupervised dimensionality‑reduction technique that creates new orthogonal features (principal components) that capture the maximum variance of the original data. It is often used as a preprocessing step for supervised models.

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