From finding what’s trending to automating all the menial tasks in Business, Machine learning is such a saving grace!

But do you know how Machine learning makes it possible? It’s through Algorithms.

Machine learning algorithms are not task-driven instead they are learning-driven which means that these ML algorithms don’t perform a task directly but rather learn how to perform the given task and this aspect serves as one of the key differentiators between ML algorithms and Conventional algorithms. If you’re one of those Machine learning Geeks, then keep reading because you will have all the Supervised ML algorithms explained in the below blog.

# What is supervised ML?

Supervised ML is a subtype of machine learning.

In this type of machine learning, a data model is fed with both the input as well as the anticipated output, unlike Unsupervised ML where there is no output fed into the machine. This is because the primary aim of supervised machine learning is to train the data machine to perform a function so that it can efficiently process a new set of data in the future.

Here’s a list of Supervised Machine algorithms you can try your hands-on

- Linear Regression – This algorithm solves regression problems.
- Logistic Regression – This algorithm solves classification problems.
- Decision Tree – This algorithm solves both regression and classification problems.
- Naive Bayes – This algorithm solves classification problems.
- SVM – This algorithm solves both regression and classification problems.
- K-Nearest Neighbour- This algorithm solves both regression and classification problems.

# Linear Regression

If you have ever heard of ML algorithms then you must have heard of Linear Regression. Linear regression is one of the most popular yet simple ML algorithms out there. This is not only an ML algorithm but a statistical algorithm as well. Linear regression is widely used to estimate user behaviour, forecast trends and predict business turnover.

Based on Supervised machine learning, this algorithm correlates the dependent variable and the independent variable using a straight line called the Regression line that is given by the equation Y=aX + b

You can use the following tools to perform Linear regression analysis

Python

MATLAB

STATA

SPSS

SAS

MINITAB

# Logistic Regression

Another supervised ML algorithm that makes it to the list of ML algorithms is Logistic regression. This regression correlates the dependent variable with a set of independent variables to estimate the probability of events by using a logistic function. Logistic regression is also referred to as the logit model. This ML algorithm estimates the discrete variables to solve the classification problems, unlike linear regression which solves only regression problems. Logistic regression plays an important role in gauging User behaviour, Customer metrics, Business patterns and a lot more.

You can use the following tools to perform a Logistic regression analysis

XLSTAT

Alteryx

R

SPSS

MINITAB

# Decision Tree

Decision trees are one of the most powerful yet popular algorithms that also fall into the supervised Machine learning category. These algorithms estimate the value of the target variable using a tree-like representation, hence they are named Decision trees. Decision trees are nothing more than a series of if-else statements. It checks to see if the condition is true, and if it is, it moves on to the next node in the decision chain. The decisions are truly based on the answers to these questions and also enable both predictive analysis as well as classification.

You can use the following tools to build decision trees

WEKA

SMILES

KNIME

Scikit-learn

# Naive Bayes

This algorithm belonging to the family of Supervised machine learning algorithms works on the Bayes principle of Conditional Probability. It is used to solve classification problems of large data sets by making independent assumptions such that the occurrence of a specific attribute is independent of the other attributes. However, the best part of this algorithm is the ability to bring real-time predictions way quicker than other algorithms and it is simple and uncomplicated, unlike other algorithms. The Naive Bayes algorithm is widely used to determine Customer and User sentiments, and KPIs help in filtering out spam emails and also enable text categorization. Lastly, it’s quite an effective algorithm for solving Natural Language processing problems.

You can use the following tools to implement the Naive Bayes algorithm

R

Python

WEKA

Monkeylearn

# SVM

SVM or Support Vector Machine is one of the most popular yet effective Supervised Machine learning algorithms.SVM enables both classifications as well as regression.

It basically classifies the data into different data points by establishing a hyperplane in an N-dimensional space. The hyperplane’s dimension varies with the number of input variables. Although, SVM algorithms are super-efficient when the number of dimensions is higher than the number of samples.

You can use the following tools for SVM algorithms,

LIBSVM

MySVM

Orange

SVM Light

# K- Nearest Neighbour

This is one of the most user-friendly and simplistic ML algorithms out there.

Like SVM, this supervised learning algorithm is capable of performing both classifications as well as regression analysis. According to this algorithm, data points that are closer to each other are similar and the ones that are farther are not similar. Hence, the key parameter for classifying new data items is the similarity index of the previously stored data items.

KNN is typically used to track User Analytics, Forecast Predictions, give suggestions and recommendations to the customer, detection of outliers and a lot of other applications.

It enables text recognition, image recognition and speech recognition as well. But, the only con of this algorithm is that there is a high dependency on the number of nearest neighbours (or) The K value.

You can use the following tools to implement KNN algorithms

GPU-FS-kNN

XLSTAT

Python

R

Scikit-Learn

That was about Supervised Machine learning algorithms. Hope you found it informative and interesting. If you’re looking to learn more about Machine learning, then make sure you check out our ML courses.

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See you with another interesting blog!