Machine learning is a rapidly growing field that has become increasingly important in today’s data-driven world.
It involves using algorithms to analyze and understand data, allowing computers to learn and make predictions or decisions without being explicitly programmed.
There are many different machine learning algorithms, each with its own strengths and weaknesses.
In this article, we will discuss the 10 most popular machine learning algorithms that data scientists and engineers use to build intelligent systems.
3 Types Of Machine Learning Algorithms
There are three main types of machine learning algorithms: supervised learning, unsupervised learning, and reinforcement learning.
1. Supervised Learning Algorithms
The Supervised Learning algorithm is used to predict an outcome or target variable based on a set of input or predictor variables.
A function is developed using these variables, which maps the inputs to the desired outputs. The model is trained using this function, and the training process continues until the desired level of accuracy is achieved on the training data.
2. Unsupervised Learning Algorithms
Unsupervised Learning algorithm is used when there is no target or outcome variable to predict or estimate.
It is mainly used for grouping similar instances together, a technique known as clustering. This technique is widely used for segmenting customers into different groups for targeted interventions. Examples of unsupervised learning algorithms include Apriori and K-means.
3. Reinforcement Learning
Reinforcement Learning algorithm trains a machine to make decisions by exposing it to an environment where it continually learns through trial and error.
The machine uses past experiences to improve and make accurate decisions. An example of this type of learning is the Markov Decision Process.
List of Machine Learning Algorithms
These algorithms include linear regression, logistic regression, decision trees, random forests, k-nearest neighbors, support vector machines, k-means, gradient boosting, neural networks, and deep learning.
Each algorithm is designed to solve different types of problems and is used in different applications.
1. Linear Regression
Linear Regression is a machine learning algorithm that models the relationship between independent and dependent variables by fitting them to a line.
This line is known as the regression line, and it can be represented by a linear equation Y= a *X + b. Think of it like arranging logs of wood in order of their weight, without weighing them.
In this equation:
- Y – Dependent Variable
- a – Slope
- X – Independent variable
- b – Intercept
The coefficients a & b are derived by minimizing the sum of the squared difference of distance between data points and the regression line.
2. Logistic Regression
Logistic Regression is a machine learning algorithm that is used to predict a binary outcome (1 or 0) based on one or more independent variables. It uses a logit function to model a probability of an event occurring. This algorithm is also known as logit regression.
These methods listed below are often used to help improve logistic regression models:
- include interaction terms
- eliminate features
- regularize techniques
- use a non-linear model
3. Decision Regression
The Decision Tree algorithm is a widely used supervised learning method for classification problems. It can handle both categorical and continuous dependent variables.
The algorithm splits the population into smaller homogeneous sets based on the most important independent variables or attributes. It is one of the most popular algorithms in machine learning.
4. SVM (Support Vector Machine) Algorithm
The Support Vector Machines (SVM) algorithm is a method for classification in which the data is plotted as points in an n-dimensional space, where n is the number of features.
Each feature is associated with a specific coordinate, making it easy to classify the data. The algorithm uses lines called classifiers to divide the data and plot it on a graph.
5. Naive Bayes Algorithms
A Naive Bayes classifier is a method that makes the assumption that the presence of a particular feature in a class is unrelated to the presence of any other feature.
Even if these features are related, the Naive Bayes classifier would consider them independently when calculating the probability of a particular outcome.
This algorithm is easy to construct and is useful for handling large datasets. It is a simple model that has been known to outperform more complex classification methods.
6. KKN (K-Nearest Neighbors) Algorithm
The k-nearest neighbors (KNN) algorithm can be applied to both classification and regression problems. It is commonly used to solve classification problems in the field of data science.
The algorithm stores all available cases and classifies new cases by taking a majority vote of its k nearest neighbors, based on a distance function.
The new case is then assigned to the class with which it has the most in common. The concept of KNN algorithm can be easily understood by comparing it to real life scenarios, for example, seeking information about a person by talking to their friends and colleagues.
Things to consider before selecting K Nearest Neighbours Algorithm:
- KNN is computationally expensive
- Variables should be normalized, or else higher range variables can bias the algorithm
- Data still needs to be pre-processed.
7. K-Means
K-means is an unsupervised learning algorithm that is used to classify datasets into a specific number of clusters, referred to as k.
The algorithm arranges the data points in a way that all the points within a cluster are similar to each other and different from the points in other clusters. It is used to solve clustering problems.
8. Random Forest Algorithms
Random Forest is an ensemble of decision trees. To classify a new object based on its attributes, each tree in the forest casts a vote for the most likely class. The class that receives the most votes is the final classification.
The trees in the forest are grown using the following process:
- A random sample of cases is selected from the training set to be used as the training set for growing each tree.
- A specified number of input variables, m, is chosen out of the total number of input variables, M, at each node. The best split on these m variables is used to split the node.
- The tree is grown to its maximum extent without pruning.
The algorithm uses a technique called bootstrap aggregating or bagging to improve the accuracy of the decision tree algorithm.
9.Dimensionality Reduction Algorithms
In today’s data-driven world, a large amount of data is collected and analyzed by various organizations such as corporations, government agencies, and research institutions.
As a data scientist, it is important to recognize that this raw data contains valuable information, but the challenge is identifying significant patterns and variables.
Dimensionality reduction algorithms, such as Decision Trees, Factor Analysis, Missing Value Ratio, and Random Forest, can assist in discovering relevant details.
10. Gradient Boosting Algorithm and Ads Boosting Algorithm
Gradient Boosting and AdaBoosting are boosting algorithms that are used when handling large amounts of data to make predictions with high accuracy.
Boosting is an ensemble learning technique that combines the predictive power of several base estimators to improve robustness. It combines multiple weak or average predictors to create a strong predictor.
These algorithms are widely used in data science competitions such as Kaggle, AV Hackathon, and CrowdAnalytix.
They are currently popular choices among machine learning practitioners and can be implemented using Python or R codes to achieve accurate results.