4 Must-Know Machine Learning Algorithms

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

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

Here are a few 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 imitate human behavior. Such algorithms can carry out complex tasks with little or zero human assistance.

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

Modern systems use several machine learning algorithms, each with its own performance benefits. Algorithms also differ in accuracy, input data, and use cases. As such, knowing which algorithm to use is the most important step to building a successful machine learning model.

1. Logistic Regression

Image of a regression graph

Also known as binomial logistic regression, this algorithm finds the probability of an event’s success or failure. It is generally the go-to method when the dependent variable is binary. Moreover, the results are usually processed as simply true/false or yes/no.

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

Logistic regression is very fast and accurate for classifying unknown records and simple data sets. It is also exceptional at interpreting model coefficients. In addition, 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 in case of one, and requires little computation power.

One 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. As a result, more powerful algorithms may better suit your more complex tasks.

2. Decision Tree

An algorithm on paper. Small boxes labelled with letters B-I are joined together with arrows, most forming a circle.

The name derives from its tree-structured approach. You can use the Decision Tree framework for classification and regression problems. Still, it’s more functional for solving classification problems.

Like a tree, it starts with the root node representing the data set. 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 terminate at leaf nodes.

Each leaf node represents the possible outcome from a build-up of decisions. Leaf nodes and decision nodes are the two major entities involved in predicting an outcome from the information given. Hence, the final output or decision is based on the dataset’s features.

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 output from the training data.

Simply put, this algorithm is a graphical representation of different options guided by preset conditions to get all possible solutions to a problem. As such, the questions asked are a build-up to arrive at a solution. Decision trees mimic the human thought process to arrive at a logical verdict using simple rules.

The major downside of this algorithm is that it is prone to instability; a minute change in data can cause a large disruption in structure. As such, you should explore various ways of obtaining consistent data sets for your projects.

3. K-NN Algorithm

Image denoting the nearest neighbor algorithm

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

As its name implies, K-Nearest Neighbor works as a classifier by assuming similarity between new and existing neighboring data. It then puts the new case in the same or similar category as the closest available data.

It is important to note that K-NN is a non-parametric algorithm; it does not make assumptions about underlying data. Also called a lazy learner algorithm, it does not immediately learn from the training data. 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 practical and people use it across various fields. In healthcare, this algorithm can predict possible health risks based on an individual’s most likely gene expressions. In finance, professionals use K-NN to forecast the stock market and even currency exchange rates.

The main disadvantage to using this algorithm is that it is more memory-intensive than other machine learning algorithms. It also has difficulty in handling complex, high-dimensional data input.

Nevertheless, K-NN remains a good choice as it adapts easily, readily identifies patterns, and allows you to modify runtime data without affecting prediction accuracy.

4. K-Means

Random green hieroglyphs fall down in vertical columns on a black background

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

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

A practical illustration of how this works involves analyzing a numbered group of footballers. You can use this algorithm to create and divide the footballers into two clusters: expert footballers and amateur footballers.

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

One consequence of using this algorithm is that the results are often inconsistent. It is order-dependent, so any change to the order of an existing data set can affect its outcome. Moreover, it lacks a uniform effect and can only handle numerical data.

Despite these limitations, K-Means is one of the best-performing machine learning algorithms. It is perfect for segmenting datasets and is trusted for its adaptability.

Choosing the Best Algorithm for You

As a beginner, you might need help settling for the best algorithm. This decision is challenging in a world full of fantastic choices. However, for a start, you should base your choice on something other than the fancy features of the algorithm.

Rather, you should consider the algorithm’s size, nature of data, task urgency, and performance requirements. These factors, among others, will help you ascertain the perfect algorithm for your project.

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