Top 10 Machine Learning Algorithms for Every Beginner

When it comes to machine learning, you’ll find a concept known as”The No Lunch” theorem. It declares that no single Machine Learning Algorithms technique is suitable for all situations and particularly relevant to supervised learning (i.e., predictive modeling).

In other words, it isn’t possible to claim it is true that neural networks are necessarily superior to decision trees or vice versa. Many variables play a role, including the size and structure of your data. Therefore you must try various approaches to your problem and use the holding-out “test collection” consisting of facts to test performance and pick the most effective one.

Always remember that the methods you use should suit the task at hand; that is where deciding on the most suitable machine learning task comes in. Similarly, if you want to clean your house, it could be a good idea to use a broom, a vacuum, or a mop, but you won’t grab a shovel and begin digging.

THE BIG PRINCIPLE

But, there’s an underlying principle common to every machine learning algorithm that is supervised by predictive modeling.

The machine learning algorithm is described by assisting in learning an objective function (f) that is the most effective way to map the input variable (X) (X) to an output (Y) that is: Y = f(X)

This is a general-learning task in which we want to predict the near future (Y) by using new variable inputs (X). We don’t know what function (f) is like or the form it takes. If we knew then, we could use it in the first place and could not learn it by studying data using machine-learning algorithms.

The most commonly used machine learning method is learning the equation of Y = f(X) to predict the value of Y to predict the new X. This is known as the predictive model or predictive analytics. We aim to create the most precise predictions we can.

For Machine learning novices keen to grasp machine learning fundamentals, here’s a brief overview of the most commonly used machine learning algorithms utilized by data scientists.

TOP MACHINE LEARNING ALGORITHMS YOU SHOULD KNOW

• Linear Regression
• Logistic Regression
• Linear Discriminant Analysis
• Classification and Regression Trees
• Naive Bayes
• K-Nearest Neighbors (KNN)
• Learning Vector Quantization (LVQ)
• Support Vector Machines (SVM)
• Random Forest
• Boosting
• AdaBoost

Linear Regression

Linear regression is one of the widely-known and understood algorithms in machine learning and statistics.

Predictive modeling is mostly concerned with reducing the errors of an algorithm and making precise predictions feasible, even intending to maximize explainability. We’ll take, reuse and steal algorithms from various areas, like statistics, and then apply them to the goal of achieving these goals.

The mathematical representation of linear regression refers to an equation that defines the line that best describes the relationship between variables that are input (x) as well as the variables that are output (y) by determining certain weightings that apply to the input variables, referred to as coefficients (B).

Linear Regression

For example: y = B0 + B1 * x

We will determine the value of y for the input x. The regression algorithm aims to discover the values of those coefficients, B0, and B1.

Different methods can be used to discover how to model linear regression using data, like an algebraic solution to linear equations for simple least squares or optimization of gradient descent.

Linear regression was used for over 200 years. It has also been extensively researched. A few good guidelines for using this technique are to eliminate variables that have a lot in common (correlated) and eliminate any noise from your data as much as possible. It’s a quick and easy method, and it is a great first method to test.

Logistic Regression

Logistic Regression is another method that machine learning has borrowed in statistical analysis. This method is preferred for binary classification issues (problems with two classes).

Logistic regression is comparable to linear regression because its goal is to find the factors that influence each input parameter. In contrast to linear regression as a traditional method, it is a forecast of the outcome that is transformed using a non-linear formula known as the logistic function.

The logistic function appears as an enormous S and transforms any value in the range 0-1. This is beneficial because it is possible to apply a rule for the results of the function to transform values into one and 0 (e.g., IF the value is less than 0.5, then output 1.) and determine an appropriate class.

Logistic Regression A graph of a logistic regression curve illustrating the likelihood of passing an examination against the number of hours spent on studying

Due to how the model is developed and adapted, logistic regression results may also be used to calculate the probability of a specific data set belonging to class 1 or class 0. This is extremely useful for issues where you must provide more explanation for the prediction.

Like logistic regression, linear regression is more effective when you eliminate attributes that do not relate to the outcome variable and attributes that are alike (correlated) to one another. It’s an easy model to master and efficient in the problem of binary classification.

Linear Discriminant Analysis

Logistic Regression can be described as a method of a classification typically restricted to two classes of classification issues. If you are dealing with multiple classes, Logistic Regression is the best choice. The Linear Discriminant Analysis algorithm is the most preferred method of linear classification.

The way to represent LDA is fairly straightforward. It comprises statistical properties of your data that are calculated for every class. A single input variable would include:

1. The value of the mean in each of the classes.
2. The variance is calculated over all levels.

The prediction is made by taking the discriminant value of each class and then making an estimate for the class with the greatest value. The method is based on the assumption that the data has the characteristics of a Gaussian distribution (bell curve), which is why it’s recommended to eliminate the outliers from your data prior. It’s a straightforward and effective method of classification-based modeling issues.

Classification and Regression Trees

Decision Trees are a key algorithm used to predict machine learning models.

A representation of the model for decision trees is a binary tree. It is a binary tree derived from algorithmic data structures and algorithms. Nothing too fancy. Each node represents one source variable (x) and the split point of the variable (assuming that the variable is numeric ).

Decision Tree

The leaf nodes in the tree are populated with one output variable (y) that can be used to make an estimate. Predictions are made by walking through the splits in the tree until reaching the leaf node and then outputting the class’s value at the leaf node.

Trees are easy to learn and extremely fast to make predictions. They are also usually reliable for various issues and don’t require any additional preparation of your information.

Naive Bayes

Naive Bayes can be described as a basic but effective algorithm for predictive modeling.

The model is comprised of two kinds of probabilities that are calculated directly from your learning data: 

1) The probability for every class, and 

2) The conditional probability of every class given every x value. After the model has been calculated, the model can formulate predictions about new data using Bayes Theorem. If your data is real-valued, it is normal to take the existence of a Gaussian distribution (bell curve) to calculate these probabilities easily.

Naive Bayes: Bayes Theorem

Naive Bayes is referred to as Naive due to the assumption that every input is completely independent. This is a shaky assumption and is not true for data that exists. However, the method can be extremely efficient in many complicated problems.

K-Nearest Neighbors

KNN is a KNN algorithm that is extremely simple and highly effective. The representation of the model used by KNN comprises the complete training data.

It searches the entire training set to find the K most closely related cases (the neighbors). It analyzes the output variables for the K instances to predict a new data point. For regression issues, it could be the average output variable; for classification issues, it could be the most common (or the most frequent) class number.

The problem lies in what you can do to determine the similarities between the instances of data. The easiest method to determine if the attributes are similar in size (all in inches, for example) is to apply a Euclidean distance, a figure you can calculate by comparing the difference between the input variables.

KNN may require a large amount of memory or storage space to hold all of the data; however, it only calculates (or learns) when the need for a prediction arises at the right time. You can also modify and improve your training sessions to keep your predictions precise.

The notion of closeness or distance can disintegrate in extremely high dimensions (lots of variables input) that can adversely affect the effectiveness of your algorithm for your particular issue. This is known as the curse of dimension. It recommends that you use only the input variables that are the most relevant for predicting what the outcome variable will be.

Learning Vector Quantization

The drawback to K-Nearest Neighbors is that you must keep the entire training data. This is because the Learning Vector Quantization algorithm (or LVQ for short) is an artificial neural network algorithm that permits users to select the number of training sessions to keep and then learn precisely what these instances should appear like.

The representation of LVQ is a set of vectors from the codebook. They are randomly selected at the beginning and then adapted to be able to summarize the training dataset through many repetitions of the algorithm for learning. After learning the codebook vectors, they can be used to predict similar K-Nearest Neighbors. The closest neighbor (best match codebook vector) is determined by taking the distance between each codebook vector and the corresponding data element. Class value (or actual amount in the case of regression) for the most closely matched unit is then returned as a prediction. To achieve the best results, change the scale of your data to be within the same range between 0 and 1.

If you notice that KNN produces good results on your training dataset, you can use LVQ to decrease the required memory to store the whole training data.

Support Vector Machines

Support Vector Machines are among the most discussed machine learning algorithms.

Hyperplanes are lines that divide the input variable space. In SVM, a hyperplane is chosen to better divide the points of that input variable space according to their class, whether class 0 or class 1. In two dimensions, you can visualize this as a line and suppose that all the input points are completely separated from this line. The SVM algorithm learns to find the coefficients that lead to the most effective separation of classes along the hyperplane.

Support Vector Machine

The space between the hyperplane and the nearest data points is referred to by its margin. The ideal or best hyperplane that can separate two classes is that with the highest margin. Only these points matter in the definition of the hyperplane and when constructing the classifier. These are the support vectors. They are the ones that define or support the hyperplane. In the real world, the optimization algorithm is employed to determine the value of coefficients that are the most effective for the margin.

SVM is one of the strongest classifiers that you can use out of the box and is worth trying with your data.

Bagging and Random Forest

Random Forest is one of the most well-known and effective machine learning techniques. It is an ensemble machine learning algorithm known as Bagging or Bootstrap Aggregation.

Bootstrap is an effective statistical technique for estimating a number from a sample of data. Like the term “mean. Take various samples from your data, calculate the mean, and average all of your mean values, giving you a more accurate estimation of the real value.

In bagging, the exact technique is utilized; however, the most common is decision trees instead of estimating whole statistical models. Some samples of your training data are then taken, and models are built for each sample of data. When you must predict the future for new data, every model makes predictions that estimate the actual output value.

Random Forest

Random Forest is an improvement to this model in which decision trees are constructed to select optimal split points for each split, and suboptimal splits are created by introducing randomness.

Therefore, the models developed for each data differ more than they normally would be, yet they are still reliable in their distinct and unique ways. Combining their predictions gives an accurate estimation of the real output value.

If you achieve good results using an algorithm that has large variability (like decision trees), it is possible to improve your results by using the algorithm.

Boosting and AdaBoost

Boosting is an approach that aims to build an efficient classifier from several weak classifiers. It is accomplished by creating an initial model using the training data and creating other models those attempt to fix the issues made by the original model. They are added until the learning data is perfect or a certain number of models is built.

AdaBoost is the first efficient boosting algorithm developed to classify binary data. It’s the most effective base for understanding how to boost. Modern boosting techniques build upon AdaBoost, a stochastic gradient boosting device.

Explanation of AdaBoost

AdaBoost is a tool used to build tiny decision trees. After the first tree has been constructed and test-driven, its performance for each training session is utilized to determine the level to which the subsequent tree will be able to pay for each event. Training data that is hard to predict will receive more weight, while events that are simple to predict events will receive less consideration. Models are created by building them sequentially, one at a time, and every time, they change the weights for the training data, which affects the learning performed by the subsequent trees. Once all the trees have been built, the models are built to accommodate the latest data, and they are assessed by evaluating the accuracy of the training data.

Because so much emphasis is given to correcting mistakes caused by algorithms, it is essential to have your data clean and outliers taken out.

Last Takeaway

A common query asked by a novice faced with a vast range of machine learning algorithms is, “which method should I choose?” The answer to the question is contingent upon several factors, including: 

(1) The size of, the quality, and the nature of the data 

(2) the computation time 

(3) How urgent is the project and 

(4) What you’re looking to accomplish using the information.

Even a seasoned data scientist cannot determine which algorithm performs best without experimenting with different algorithms. While there are a variety of Machine Learning algorithms, these are the most popular. If you’re new to Machine Learning, this would be an excellent start to begin learning.