The ultimate compilation: 7 top Machine Learning algorithms

As the field of Machine Learning continues to evolve, staying up-to-date with effective algorithms is crucial for success.

In this blog, we’ll explore the top 7 Machine Learning algorithms of all time. From tried-and-tested classics to state-of-the-art innovations, we’ll walk you through every algorithm revolutionizing the space of business intelligence.

Whether you’re an experienced data scientist or a newbie dipping your toes into the exciting world of Machine Learning, this list is all you need to shape the future of AI. 

So, without further ado , let’s get started.

What is a Machine Learning algorithm?

Prior to sharing the list, let’s pause to address a simple yet crucial question: what is a Machine Learning algorithm?

At its simplest, a Machine Learning model is a specialized program, empowering computers to sift through data, unearth hidden patterns, and make judgements or predictions about new data it hasn’t encountered before.

Machine Learning algorithms aren’t explicitly programmed. These algorithms are self-modifying, self-improving programs, feeding on a tsunami of data every passing hour and automatically acclimatizing themselves to new conditions or scenarios.

Machine Learning algorithms are classified into four categories:

• Supervised learning
• Unsupervised learning
• Semi-supervised learning
• Reinforcement learning

Refer to the table below to discover the key features, merits, and limitations of each of the aforementioned types.

Best Machine Learning algorithms: Our best picks

Let's now dive into the top machine-learning algorithms that are shaping the field and maximizing innovation. 

1. Naive Bayes

Naive Bayes, a family of supervised learning algorithms, simplifies predictive modeling by relying on Bayes’ Theorem of Probability.

Naive Bayes’ algorithm calculates conditional probabilities and assumes classifications based on the combined cues observed in the data. In simple terms, it meticulously analyzes the available data, establishes patterns of contrasts and commonalities, and consistently makes precise predictions.

Let’s take up an example!

Suppose you have a big bag of marbles, some red and some blue. Naive Bayes will examine the color of each marble and make a guess whether it’s red or blue while looking into how many red or blue marbles it has seen previously. It’s similar to saying: “I’ve seen and know how red marbles look, and this one, right here, is probably red too.”

Basically, Naive Bayes will make an educated guess about new things by looking at what it has learned from past experiences. 

Naive Bayes Pros and Cons

• Pros: Simple; Fast; Handles high dimensions
• Cons: Assumes independence; Can be inaccurate

2. Random Forest

A random forest algorithm uses an assembly of decision trees to represent and classify statistical probabilities.

Leveraging a technique called ‘bagging,’ the algorithm trains each tree on a random sampling of data. It gauges and tallies up opinions and picks the most popular one as the ultimate answer.

Imagine you’ve got to choose whether or not to go for a picnic depending on the weather conditions. You turn to your friends, and each has a different opinion. Some believe it’s going to be ‘sunny,’ while others say that it’s going to be rainy today. You count their votes—and if most of them say ‘Yes,’ you plan to go. This is just how a random forest algorithm works. 

Random Forest Pros and Cons

• Pros: High accuracy; Handles large datasets
• Cons: Slow training; Complex interpretation

3. Linear Regression

Linear regression, a supervised learning algorithm, aids in predicting and forecasting values within a continuous range. 

Having its roots in statistics, a linear regression algorithm maps and establishes the relationship based on how a dependent variable changes when the independent variable shifts or varies.

Picture this: you’re a lemonade seller, and you’d like to predict how much money you’ll likely make depending on the number of cups you sell. On tracking your sales for a week, this is how your numbers look: 10 cups sold on Monday, 12 on Tuesday, and so on.

Next, you use linear regression to map these sales. The algorithm draws out a straight line that passes through data points on a graph. These data points represent your weekly earnings, emphasizing each variation corresponding to changes in the number of lemonade cups sold.

Linear Regression Pros and Cons

• Pros: Simple; Interpretable; Fast predictions
• Cons: Assumes linearity; Sensitive to outliers

4. Logistic Regression

Logistic regression, also known as ‘logit regression,’ helps in binary classification. Simply put, it categorizes elements of a set into one of two categories or classes. 

Originally rooted in statistical methodologies, logistic regression, as a supervised learning model, estimates the probability of a data input belonging to either category. When an event is analyzed through the logit regression algorithm - and it aligns with classification rules - it’s classified as 1. If it doesn’t meet the criteria, it’s marked as 0.

Here’s an example to help you grasp.

Think of a situation where you’ve to identify whether an email is spam or not. A logistic regression model—trained on a dataset with values labeled as spam and non-spam emails—builds a classification rule based on the content and characteristics of emails and assigns probabilities to new incoming ones. 

If the probability exceeds the threshold, the email is a spam entry. Otherwise, it’s classified as non-spam. 

Logistic Regression Pros and Cons

• Pros: Interpretable; Fast training; Probabilistic predictions
• Cons: Assumes linearity; Not for non-linear data

5. K-Means

K-Means aids in cluster analysis. As an unsupervised learning algorithm, it classifies data into clusters on the basis of internal similarities and dissimilarities from peer clusters.

Flummoxed? Let us help!

Suppose you’ve a basket of fruits, containing apples, oranges, and bananas. Applying K-Means clustering, you can sort them automatically among two categories: one with round fruits (apples and oranges) and another with elongated fruits (bananas). This is perhaps the easiest way to have a hang around how K-Means algorithm works, organizing similar items depending on internal homogeneity and external heterogeneity.

The K-Means algorithm saves the day when complex datasets, having intricate nuances and patterns, need to be classified. The central motive is to slim down the probabilities of finding in-cluster variance, helping decision-makers make more meaningful and interpretive decisions. 

K-Means Pros and Cons

• Pros: Simple; Fast; Scalable
• Cons: Sensitive to initialization; Requires number of clusters

6. Support Vector Machine (SVM)

Support Vector Machine, or SVM, facilitates classification of data using a ‘hyperplane.’ The algorithm divides a dataset into multiple categories or classes by finding the best possible line (or boundary), which is referred to as a hyperplane. 

But, here’s a catch: when marking up a line, SVM looks for a path that maximizes the distance between the closest points for each data group. This means, every value of data stands out for decision-makers to interpret, even if they are all jumbled up. 

Read this example to see what we mean.

Imagine you’ve a pile of dots on a paper, some red and some blue, all mixed and clumped together. If you draw a line, separating the red dots from the blue ones, it gives the little support you need to clear things a bit. This is exactly what SVM comes in handy for in Machine Learning. 

SVM Pros and Cons

• Pros: Effective in high dimensions; Works with non-linear data
• Cons: Slow on large datasets; Parameters hard to interpret

7. K-Nearest Neighbors (KNN) Algorithm

The KNN algorithm can be utilized to address both classification and regression problems. It works on a simple principle: consolidating every available case in a dataset and classifying the new one by taking a majority vote of its K neighbors.

In layman’s terms, when a new input of data is received, KNN analyzes the available data points with similarities and assigns the new point to the most common category. 

Let us provide an example to make it all click. 

Imagine you’ve a group of friends, residing in different suburbs. A new individual moves in, and you’re wondering which suburb he might like based on where your friends live. KNN functions on similar lines. It refers to data points with features (neighborhoods with amenities), with every point belonging to a certain category (preferences).

Whenever a new data point taps in, it navigates through the available data points and aligns the new instance with the category that sticks out the most common and relatable.

KNN Pros and Cons

• Pros: Simple; No training time; Handles noisy data
• Cons: Computationally expensive; Sensitive to irrelevant features

Final word

The world of Machine Learning algorithms is vast and dynamic, offering a wide variety of tools for diverse applications. By harnessing the strengths of each algorithm, practitioners can unlock new insights, drive innovation, and pave the way for transformative outcomes in artificial intelligence.

As we navigate the complexities of modern data science, understanding the nuances of these algorithms empowers us to tackle challenges, drive meaningful outcomes, and shape the future of AI-driven technologies.