4 must-have machine learning algorithms

Have you ever wondered how Netflix’s self-driving cars, chatbots and automated recommendations work? These practical technological advances are the products of machine learning.

This type of artificial intelligence trains computers to study human behavior and use algorithms to make intelligent decisions without intervention. The algorithms learn independently from input data and predict the logical output based on the dynamics of a set of training data.

Here are some of the best machine learning algorithms that help create and train intelligent computer systems.

The Importance of Algorithms in Machine Learning

A machine learning algorithm is a set of instructions used to help a computer mimic human behavior. Such algorithms can perform complex tasks with little or no human assistance.

Instead of writing code for each task, the algorithm builds logic from the data you feed into the model. Given a large enough data set, it identifies a pattern, allowing it to make logical decisions and predict the value output.

Modern systems use multiple machine learning algorithms, each with their own performance advantages. Algorithms also differ in terms of accuracy, input data, and use cases. As such, knowing which algorithm to use is the most important step in creating a successful machine learning model.

1. Logistic regression

Image of a regression graph

Also known as binomial logistic regression, this algorithm determines the probability of an event succeeding or failing. This is usually the go-to method when the dependent variable is binary. Also, the results are usually treated as simply true/false or yes/no.

To use this statistical model, you must study and categorize labeled data sets into discrete categories. An awesome feature is that you can extend logistic regression to multiple classes and give a realistic view of probability-based class predictions.

Logistic regression is very fast and accurate for classifying unknown records and simple data sets. It is also exceptional for interpreting model coefficients. Also, logistic regression works best in scenarios where the data set is linearly separable.

With this algorithm, you can easily update models to reflect new data and use inference to determine the relationship between features. It is also less prone to overfitting, has a regularization technique when needed, and requires little computing power.

A big limitation of logistic regression is that it assumes a linear relationship between dependent and independent variables. This makes it unsuitable for nonlinear problems because it only predicts discrete functions using a linear decision surface. Therefore, more powerful algorithms may be better suited to your more complex tasks.

2. Decision tree

An algorithm on paper.  Small squares labeled with the letters BI are connected by arrows, most forming a circle.

The name derives from its tree-like approach. You can use the decision tree framework for classification and regression problems. Still, it is more functional in solving classification problems.

Like a tree, it starts with the root node representing the dataset. The branches represent the rules guiding the learning process. These branches, called decision nodes, are yes or no questions that lead to other branches or end at leaf nodes.

Each leaf node represents the possible outcome of an accumulation of decisions. Leaf nodes and decision nodes are the two main entities involved in predicting an outcome from the provided information. Therefore, the final output or decision is based on the characteristics of the data set.

Decision trees are supervised machine learning algorithms. These types of algorithms require the user to explain what the input is. They also need a description of the expected outcome of the training data.

In simple terms, this algorithm is a graphical representation of different options guided by predefined conditions to obtain all possible solutions to a problem. As such, the questions posed are an accumulation to arrive at a solution. Decision trees mimic the human thought process to arrive at a logical verdict using simple rules.

The main disadvantage of this algorithm is that it is subject to instability; a tiny change in the data can lead to a big disruption of the structure. As such, you should explore various ways to get consistent data sets for your projects.

3. K-NN Algorithm

Image showing nearest neighbor algorithm

K-NN has proven to be a useful multi-faceted algorithm for solving many real-world problems. Although it is one of the simplest machine learning algorithms, it is useful for many industries, from security to finance and economics.

As the name suggests, K-Nearest Neighbor works as a classifier by assuming similarity between new and existing neighbor data. It then places the new case in the same or similar category to the nearest available data.

It is important to note that K-NN is a nonparametric algorithm; it makes no assumptions about the underlying data. Also called lazy learning algorithm, it does not learn from the training data immediately. Instead, it stores current data sets and waits until it receives new data. Then it performs classifications based on proximity and similarities.

K-NN is convenient and people use it in various fields. In health, this algorithm can predict potential health risks based on an individual’s most likely gene expressions. In finance, professionals use K-NN to predict the stock market and even currency rates.

The main disadvantage of using this algorithm is that it is more memory intensive than other machine learning algorithms. It also has difficulty handling complex and large data entries.

Nevertheless, K-NN is still a good choice because it scales easily, easily identifies patterns, and allows you to modify runtime data without affecting prediction accuracy.

4. K-Means

Random green hieroglyphs fall in vertical columns on black background

K-Means is an unsupervised learning algorithm that groups unlabeled data sets into unique clusters. It receives inputs, minimizes the distance between data points, and aggregates data based on common points.

For clarity, a cluster is a set of data points grouped into one due to some similarity. The “K” factor tells the system how many clusters it needs.

A practical illustration of how this works is to analyze a numbered group of footballers. You can use this algorithm to create and divide footballers into two groups: expert footballers and amateur footballers.

The K-Means algorithm has several real applications. You can use it to categorize student grades, perform medical diagnoses, and display search engine results. In summary, it excels at analyzing large amounts of data and dividing it into logical clusters.

A consequence of using this algorithm is that the results are often inconsistent. It depends on the order, so any change in the order of an existing dataset can affect its result. Also, it lacks a uniform effect and can only handle digital data.

Despite these limitations, K-Means is one of the most successful machine learning algorithms. It is perfect for segmenting datasets and is known for its adaptability.

Choose the best algorithm for you

As a beginner, you may need some help choosing the best algorithm. This decision is difficult in a world full of fantastic choices. However, to start with, you should base your choice on something other than the sophisticated features of the algorithm.

Instead, you need to consider the size of the algorithm, the nature of the data, the urgency of the tasks, and the performance requirements. These and other factors will help you determine the perfect algorithm for your project.

Sharon D. Cole