Introduction to Machine Learning Algorithms

To achieve the outputs necessary for today’s technology, data scientists must follow several steps:

Which algorithm you choose for your project will be dependent on the type of data you use. Whether it be nominal, binary, ordinal or interval, machine learning can find valuable insights.

There are three main sets of machine learning algorithms: Supervised and unsupervised, including their ever-growing number of subtypes, and reinforcement learning algorithms. 

Most machine learning uses supervised learning algorithms, which are indicated by the use of labeled data (such as time and weather) that entails both input (x) and output (y) variables. You, as the “teacher,” know the correct answer(s) and supervise the algorithm as it makes predictions based on the training data. If necessary, you make corrections until the algorithm achieves an adequate level of execution. 

Although there are a variety of supervised machine learning algorithms, the most commonly used include:

Unsupervised machine learning algorithms are used for unstructured data to find common characteristics and distinct patterns in the dataset. Because this type of ML algorithm does not require prior training or labeled data, it is free to explore the structure of the information. 

Similar to supervised machine learning algorithms, there are several types of unsupervised algorithms, such as kernel methods and k-means clustering. 

A simple variable linear regression technique is a type of ML algorithm that demonstrates how a single input-independent variable (feature variable) and an output-dependent variable work together. 

More common is the multivariable linear regression algorithm, which determines the relationship between multiple input variables and an output variable. Regression models are intended to be used with real values such as integers or floating-point values (quantities, amounts and sizes). 

Advantages: Quick to model. Simple to understand. Useful for smaller datasets that aren’t overly complicated.

Disadvantages: Difficult to design for nonlinear data. Tends to be ineffectual when working with highly complex data.

An alternative regression machine learning algorithm is the logistic model. This technique is designed for binary classification problems, as indicated by two possible outcomes that are affected by one or more explanatory variables. 

Simple to interpret and versatile in its uses, logistic regression is ideal for applications where interpretability and inference are vital, such as fraud detection.

Advantages: Easy to implement and interpret. Suited well for a linearly separable dataset.

Disadvantages: An excessive amount of data creates a complex model that can lead to overfitting in high-dimensional datasets (number of features is higher than observations). Logistic regression assumes linearity between the dependent and independent variables.

This class of powerful machine learning algorithms is capable of achieving high levels of accuracy and is highly interpretable. Knowledge learned by a decision tree algorithm is expressed as a hierarchical structure, or “tree,” complete with various nodes and branches. 

Each decision node represents a question about the data, and the branches that stem from a node represent possible answers. A secondary type of node, which is less certain in its responses, is a chance node. An end node is indicated at the end of the decision-making process. 

Decision tree machine learning algorithms can be used to solve both classification and regression problems, often referred to as CART. A decision tree technique is useful at identifying trends. 

Advantages: Easy to explain. Does not require normalization or scaling of data.

Disadvantages: Can lead to overfitting. Affected by noise (distortions in the information can cause the algorithm to miss patterns in the data). Not suitable for large datasets.

A random forest machine learning algorithm is considered an ensemble method because it is a collection of hundreds and sometimes thousands of decision trees. The model increases predictive power by combining the decisions of each decision tree to find an answer. The random forest algorithm learns how to classify unlabeled data by using labeled data. 

The random forest technique is simple, highly accurate and widely used by engineers.

Advantages: Applicable for both regression and classification problems. Efficient on large datasets. Works well with missing data. 

Disadvantages: Not easily interpretable. Can cause overfitting if noise is detected. Slower than other models at creating predictions.

This subset of machine learning is inspired by the neural networks within the human brain. A neural network machine learning algorithm is built with artificial neurons spread throughout three or more layers, which provides the observer with a greater amount of data in a more detailed and distinct way. 

Because of these several layers and the fact that the process is human-like, the neural network machine learning algorithm is regarded as deep learning. Real-world applications include Apple’s Face ID, and it is the power behind GoogLeNet and Google search engine results.

Neural networks can be utilized for regression problems and are ideal for dealing with high-dimensional issues like speech and object recognition.

Advantages: Provides better results with an extensive amount of data. Able to work with incomplete information. Parallel processing ability.

Disadvantages: Requires much more data than other machine learning algorithms. The method has a “black box” nature, which means we do not know how or why the model came up with a particular output. Computationally expensive.

Kernel methods are a group of supervised or unsupervised machine learning algorithms used for pattern analysis. They locate and examine general types of relations, such as rankings, clusters or classifications in datasets, and separate the data points between two categories. The most popular kernel method application is the support vector machine (SVM). 

Kernel functions work in graphs, text, images, vectors and sequential data. They can help turn any linear model into a nonlinear model when instance-based learning is needed.

Advantages: Effective in high-dimensional spaces. Unlikely to overfit. Versatile. Useful in data mining.

Disadvantages: Complex, which requires a high amount of memory. Does not scale well to larger datasets. Random forest is typically preferred over SVMs.

The simple k-means clustering technique is one of the most popular unsupervised machine learning algorithms. Its objective is to place (n) observations into a number of clusters (k). Each group contains observations, or data points, that have similar features, while k serves as the prototype of each. The purpose of this technique is to minimize within-cluster variances.

Fields that utilize this type of machine learning algorithm include data mining, marketing, science, city planning and insurance.

Advantages: Relatively simple to implement. Adapts to new examples. Scales to large datasets. 

Disadvantages: Sensitivity to scale. Can only be used with numeric data. You must determine the number of clusters. Lacks consistency.

Data scientists organize, analyze and report on the large datasets they receive. The data can come from any industry, and it’s their job to try to predict and plan for the future. While they have many tools to help them analyze this data, machine learning algorithms help data scientists realize valuable insights faster. As data around the world continues to multiply and new algorithms are introduced, you can expect the fields of data science and machine learning to further expand to keep pace.