5 Machine Learning Classification Algorithms You Need to Know

5 Machine Learning Classification Algorithms You Need to Know

In this article, we will explore five machine learning classification algorithms that you should be aware of. We’ll also discuss some of the pros and cons of each algorithm.

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In machine learning, there are generally two types of tasks: regression and classification. In a regression task, we are trying to predict a continuous output, such as price or probability. In a classification task, we are instead trying to predict a discrete label, such as spam/not spam or rain/no rain.

There are many different classification algorithms, but in this article, we will focus on five of the most common: logistic regression, decision trees, support vector machines, random forest, and k-nearest neighbors.

Logistic regression is a linear model that is used for binary classification (predictions of either 0 or 1). It works by calculating a linear combination of the input features (x) and weights (w), plus a bias term (b):

ŷ= w*x + b

The output ŷ will be between 0 and 1 (due to the sigmoid function), which can be interpreted as the probability that the input x belongs to class 1.

Decision trees are non-linear models that are used for both binary and multi-class classification. They work by creating a series of if-then-else rules based on the input features. The tree starts with a single node (the root node), andsplitsthe data into two groups based on some rule. This process is repeated recursively until each group only contains data points with the same label.

Support vector machines (SVMs) are also non-linear models that can be used for both binary and multi-class classification. An SVM works by finding the hyperplane that maximizes the margin between two classes. The margin is defined as the distance between the closest points from each class to the hyperplane. The figure below shows three possible hyperplanes (represented by dashed lines) between two classes:

The SVM chooses the one with the largest margin. If you have more than two classes, SVMs can still be used by using something called one-versus-the rest(OvR). This involves training one SVM per class, with all other classes being treated as negative examples — so if you have five classes then you would train five SVMs, each trying to distinguish that class from all others.

Random forest is an ensemble method that uses multiple decision trees to make predictions. It works by training each decision tree on a different subset of featuresand then averaging their predictions:

predictions = [tree1(input_features), tree2(input_features), tree3(input_features)]
final_prediction = mean(predictions)

Linear Classifiers

Machine learning is a vast and growing field with many different sub-disciplines. In this article, we will focus on the area of supervised learning, and specifically on the problem of classification. Supervised learning algorithms are those which take a set of training data consisting of input examples along with their corresponding desired outputs, and produce a generalizable model which can then be used to make predictions on new data. The task of classification is to take an input example and predict its corresponding output class.

There are many different supervised learning algorithms for solving the classification problem, but in this article we will focus on five of the most popular: support vector machines, logistic regression, decision trees, naive Bayes, and k-nearest neighbors. We will briefly describe each algorithm and discuss when it is best to use each one.

Linear Classifiers:
Support Vector Machines:
Logistic Regression:
Decision Trees:
Naive Bayes:
k-Nearest Neighbors:

Support Vector Machines

Support Vector Machines (SVM) are a type of supervised machine learning algorithm that can be used for both classification and regression tasks. The goal of an SVM is to find the best possible line or decision boundary that can separate different classes of data. For example, you could use an SVM to classify images as either “cat” or “not cat.”

SVMs are a popular choice for machine learning because they offer several advantages over other algorithms, including:

-Ability to handle both linear and nonlinear data
-Good performance on small datasets
-Not susceptible to overfitting

There are also some disadvantages to using SVMs, including:

-Computationally expensive, so they may not be practical for very large datasets
-Difficult to interpret results

Decision Trees

Decision trees are a supervised learning algorithm used for both, classification and regression tasks where we will trying to predict a target variable based on several features.

A decision tree is built top-down from a root node and involves partitioning the data into subsets that contain instances with similar values (classifications).

We can use Decision Trees for both categorical and numerical target variables. They are mostly used in areas where the target variable takes a finite set of values. For example, if we are predicting whether an email is spam or not, the target variable here would be binary (1 or 0).

CART (Classification and Regression Trees) is the most common algorithm used to build decision trees. It works by first splitting the data into two groups using a single feature k and a threshold value tk. The split is done so that instances with values greater than tk are in one group (right) and those with values less than or equal to tk are in another group (left).

Ensemble Methods

Ensemble methods are Machine Learning algorithms that use multiple models to make predictions. Ensemble methods are powerful because they can improve the accuracy of predictions by combining the strengths of multiple models. There are several different types of ensemble methods, but the most popular ones are bagging, boosting, and stacking.

Bagging is a type of ensemble method that trains multiple models on different subsets of the data and then averaged the predictions of each model. Boosting is another type of ensemble method that trains multiple models sequentially, with each new model correcting the errors of the previous one. Stacking is an ensemble method that combines the predictions of multiple models using a meta-learner.

Ensemble methods are often used in competition because they can provide a significant accuracy boost over traditional Machine Learning algorithms. If you’re interested in using ensemble methods in your own projects, there are many great libraries available in Python, R, and MATLAB.

Neural Networks

Neural networks are a type of machine learning algorithm that are particularly well suited for classification tasks. Neural networks are similar to other machine learning algorithms, but they are composed of a large number of interconnected processing nodes, or neurons, that can learn to recognize patterns of input data.

Neural networks have been used for a variety of classification tasks, including identifying objects in images, facial recognition, and text classification.


Lastly, machine learning is a powerful tool that can be used to automatically classify data. In this article, we’ve seen five of the most popular machine learning classification algorithms: logistic regression, decision trees, random forests, support vector machines, and neural networks. Each algorithm has its own strengths and weaknesses, so it’s important to choose the right algorithm for your particular problem.

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