Top 5 Commonly Used Supervised Machine Learning Algorithms
Machine learning algorithms can let machines do surgery, play chess, and become more intelligent and human-like. We are in an era of continual technological advancement, and by seeing how computers have developed through time, we may make predictions about what will happen in the future.
The democratization of computer tools and methods is among the revolution's key distinguishing characteristics. Data scientists have created powerful data-crunching computers during the last five years by effortlessly implementing cutting-edge methodologies. The outcomes are astonishing.
Supervised machine learning algorithms are the common ways to solve problems in supervised classification. Supervised machine learning algorithms operate on both sets of data by finding patterns within supervised learning algorithms to classify new unseen unlabeled datasets.
Table of Contents
5 COMMON MACHINE LEARNING ALGORITHMS
- Linear Regression
One of the well-understood algorithms in statistics and machine learning is linear regression. At the price of explainability, predictive modeling primarily focuses on reducing a model's error or producing the most precise forecasts.
- Logistic Regression
Don't be misled by the name! It is a classification method rather than a regression one. Based on a collection of independent variables, it estimates discrete values (binary values like 0/1, yes/no, and true/false) (s). It determines the likelihood that an event will occur by fitting data to a logit function. It is known as logit regression as a result. As a result of predicting the likelihood, its output values range from 0 to 1.
It is a technique for unsupervised learning that addresses clustering issues. Data sets get divided into a certain number of clusters— let's call it K—in such a way that each cluster's data points are homogeneous and heterogeneous from those in the other clusters.
- Decision Tree
Nowadays, a widely used machine learning algorithm is the decision tree algorithm, a supervised learning technique used to categorize issues. For both categorical and continuous dependent variables, it performs well when categorizing.
- Dimensionality Reduction Algorithms
Businesses, governments, and research institutions store and analyze enormous volumes of data in the modern world. As a data scientist, you know that a wealth of information gets included in this raw data; the difficult part is identifying important patterns and variables.
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