Fundamentals of Machine Learning Algorithms

(Decision Trees, SVM, KNN, and More)

At the heart of machine learning (ML) are algorithms the set of rules and techniques that enable computers to learn from data and make decisions.
While there are hundreds of algorithms, a few fundamental ones form the building blocks of the field. Let’s explore three core types: Decision Trees, Support Vector Machines (SVM), and K-Nearest Neighbors (KNN).

2 Decision Trees

What It Is:
A Decision Tree is a model that uses a branching method to illustrate every possible outcome of a decision. It’s like a flowchart where each internal node represents a decision based on a feature, each branch represents an outcome, and each leaf node represents a final decision or classification.

How It Works:

1 It splits the dataset into subsets based on feature values.

2 It keeps splitting until it reaches a stopping condition (like all points belong to one class or a maximum depth).

Pros:

1 Easy to understand and interpret.

2 Handles both numerical and categorical data.

3 Requires little data preprocessing.

Cons:

1 Prone to overfitting (model becomes too complex and specific to the training data).

2 Sensitive to small changes in data.

Example Use:

1 Deciding if a customer will buy a product based on age, income, and browsing behaviour.

3 Support Vector Machines (SVM)

What It Is:
A Support Vector Machine tries to find the best boundary (or “hyperplane”) that separates data into different classes. The goal is to maximize the margin the distance between the boundary and the closest data points (called support vectors).

How It Works:

1 Maps input data into high-dimensional space.

2 Finds the hyperplane that best separates the categories.

3 Can use “kernel functions” to handle nonlinear separations.

Pros:

1 Highly effective in high-dimensional spaces.

2 Works well with a clear margin of separation.

3 Versatile with different kernel functions (linear, polynomial, radial basis function).

Cons:

1 Not suitable for very large datasets (computationally expensive).

2 Less effective when classes overlap significantly.

Example Use:

1 Classifying whether an email is spam or not based on word frequency features.

4 K-Nearest Neighbors (KNN)

What It Is:
K-Nearest Neighbors is one of the simplest machine learning algorithms. It classifies a data point based on how its neighbors are classified.

How It Works:

1 Choose a value for “K” (number of neighbors to consider).

2 Measure the distance (usually Euclidean) between the new data point and all other points.

3 Assign the most common label among the K closest neighbors.

Pros:

1 Simple and intuitive.

2 No training phase (instance-based learning).

3 Works well for small datasets.

Cons:

1 Slow with large datasets (computes distance to every other point).

2 Sensitive to the choice of K and to noisy data.

Example Use:

1 Recommending products based on what similar users have bought.

Conclusion

Understanding the basics of these fundamental algorithms Decision Trees, SVM, and KNN gives you a strong foundation in machine learning. Each has its strengths and is suited for different types of problems. The best algorithm often depends on your specific data, the problem you’re solving, and the performance you require.