Machine Learning Models Explained

Welcome to a simplified guide to machine learning models! This article provides a brief explanation of the different types of machine learning models, categorized as either supervised or unsupervised.

Supervised Learning

Supervised learning involves training a model to map an input to an output based on example inputoutput pairs. For instance, you could predict shoe size based on age using a dataset of age and shoe size data.

Regression

In regression models, you find a target value based on independent predictors. This helps find the relationship between dependent and independent variables. The output in regression is continuous. Here are some common types:

• Linear Regression: Finding a line that best fits the data. Extensions include multiple linear regression (finding a plane of best fit) and polynomial regression (finding a curve for best fit).
• Decision Tree: A treelike model where each node represents a decision. More nodes generally lead to greater accuracy.
• Random Forest: An ensemble learning technique using multiple decision trees. It creates these trees using bootstrap datasets and randomly selects a subset of variables at each step. By relying on the majority prediction, it reduces the risk of error.
• Neural Network: A multilayered model inspired by the human brain, with input, hidden, and output layers. Each node in the hidden layer performs a function that leads to the output.

Classification

In classification, the output is discrete. Common types include:

• Logistic Regression: Similar to linear regression but models the probability of a finite number of outcomes (typically two).
• Support Vector Machine (SVM): A technique that finds a hyperplane in ndimensional space to distinctly classify data points.
• Naive Bayes: A probabilistic machine learning model for classification based on Bayes' theorem.
• Decision Trees, Random Forests, and Neural Networks: These models follow the same logic as previously explained, but the output is discrete instead of continuous.

Unsupervised Learning

Unsupervised learning is used to draw inferences and find patterns from input data without labeled outcomes.

Clustering

Clustering involves grouping data points. It's used for customer segmentation, fraud detection, and document classification. Common techniques include kmeans clustering, hierarchical clustering, meanshift clustering, and densitybased clustering. All aim to achieve the same goal: finding clusters.

Dimensionality Reduction

Dimensionality reduction reduces the number of features in your dataset. It can be achieved through feature elimination or feature extraction. A popular method is Principal Component Analysis (PCA).

Conclusion

This was a brief overview of machine learning models. Each model has its own complexities, which will be covered in future videos. Be sure to subscribe to stay updated!