Linear Regression vs Logistic Regression | Edureka
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YouTube: https://youtu.be/OCwZyYH14uw ** Data Science Certification using R: https://www.edureka.co/data-science ** This Edureka PPT on Linear Regression Vs Logistic Regression covers the basic concepts of linear and logistic models. The following topics are covered in this session: Types of Machine Learning Regression Vs Classification What is Linear Regression? What is Logistic Regression? Linear Regression Use Case Logistic Regression Use Case Linear Regression Vs Logistic Regression Blog Series: http://bit.ly/data-science-blogs Data Science Training Playlist: http://bit.ly/data-science-playlist Follow us to never miss an update in the future. YouTube: https://www.youtube.com/user/edurekaIN Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
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Logistic regression
Logistic regression is a machine learning classification algorithm that predicts the probability of a categorical dependent variable. It models the probability of the dependent variable being in one of two possible categories, as a function of the independent variables. The model transforms the linear combination of the independent variables using the logistic sigmoid function to output a probability between 0 and 1. Logistic regression is optimized using maximum likelihood estimation to find the coefficients that maximize the probability of the observed outcomes in the training data. Like linear regression, it makes assumptions about the data being binary classified with no noise or highly correlated independent variables.
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This Logistic Regression Presentation will help you understand how a Logistic Regression algorithm works in Machine Learning. In this tutorial video, you will learn what is Supervised Learning, what is Classification problem and some associated algorithms, what is Logistic Regression, how it works with simple examples, the maths behind Logistic Regression, how it is different from Linear Regression and Logistic Regression applications. At the end, you will also see an interesting demo in Python on how to predict the number present in an image using Logistic Regression.
Below topics are covered in this Machine Learning Algorithms Presentation:
1. What is supervised learning?
2. What is classification? what are some of its solutions?
3. What is logistic regression?
4. Comparing linear and logistic regression
5. Logistic regression applications
6. Use case - Predicting the number in an image
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About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
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Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
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What skills will you learn from this Machine Learning course?
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1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
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3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
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Classification Based Machine Learning Algorithms
This slide focuses on working procedure of some famous classification based machine learning algorithms
Ml3 logistic regression-and_classification_error_metrics
This document provides an introduction to logistic regression. It begins by explaining how linear regression is not suitable for classification problems and introduces the logistic function which maps linear regression output between 0 and 1. This probability value can then be used for classification by setting a threshold of 0.5. The logistic function models the odds ratio as a linear function, allowing logistic regression to be used for binary classification. It can also be extended to multiclass classification problems. The document demonstrates how logistic regression finds a decision boundary to separate classes and how its syntax works in scikit-learn using common error metrics to evaluate performance.
Linear Regression and Logistic Regression in ML
Linear regression and logistic regression are statistical modeling techniques. Linear regression predicts continuous dependent variables using independent variables, while logistic regression predicts binary dependent variables. Both aim to model relationships between variables by estimating coefficients. Logistic regression models the log odds of the dependent variable rather than the variable directly. Key evaluation metrics for regression include accuracy, precision, recall, and F1 score, which are calculated using a confusion matrix.
Regression (Linear Regression and Logistic Regression) by Akanksha Bali
Regression analysis is a statistical technique used to examine relationships between variables. Linear regression finds the best fitting straight line through data points to model the relationship between a continuous dependent variable (Y) and one or more independent variables (X). Logistic regression produces results in a binary format to predict outcomes of categorical dependent variables. It transforms the linear equation using logarithms to restrict predicted Y values between 0 and 1.
Support vector machines (svm)
A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples. In two dimentional space this hyperplane is a line dividing a plane in two parts where in each class lay in either side.
Logistic regression with SPSS
Logistic regression is used to predict categorical outcomes. The presented document discusses logistic regression, including its objectives, assumptions, key terms, and an example application to predicting basketball match outcomes. Logistic regression uses maximum likelihood estimation to model the relationship between a binary dependent variable and independent variables. The document provides an illustrated example of conducting logistic regression in SPSS to predict match results based on variables like passes, rebounds, free throws, and blocks.
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Logistic regression
Logistic regression is a machine learning classification algorithm that predicts the probability of a categorical dependent variable. It models the probability of the dependent variable being in one of two possible categories, as a function of the independent variables. The model transforms the linear combination of the independent variables using the logistic sigmoid function to output a probability between 0 and 1. Logistic regression is optimized using maximum likelihood estimation to find the coefficients that maximize the probability of the observed outcomes in the training data. Like linear regression, it makes assumptions about the data being binary classified with no noise or highly correlated independent variables.
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Below topics are covered in this Machine Learning Algorithms Presentation:
1. What is supervised learning?
2. What is classification? what are some of its solutions?
3. What is logistic regression?
4. Comparing linear and logistic regression
5. Logistic regression applications
6. Use case - Predicting the number in an image
What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
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About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
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Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
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What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
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Classification Based Machine Learning Algorithms
This slide focuses on working procedure of some famous classification based machine learning algorithms
Ml3 logistic regression-and_classification_error_metrics
This document provides an introduction to logistic regression. It begins by explaining how linear regression is not suitable for classification problems and introduces the logistic function which maps linear regression output between 0 and 1. This probability value can then be used for classification by setting a threshold of 0.5. The logistic function models the odds ratio as a linear function, allowing logistic regression to be used for binary classification. It can also be extended to multiclass classification problems. The document demonstrates how logistic regression finds a decision boundary to separate classes and how its syntax works in scikit-learn using common error metrics to evaluate performance.
Linear Regression and Logistic Regression in ML
Linear regression and logistic regression are statistical modeling techniques. Linear regression predicts continuous dependent variables using independent variables, while logistic regression predicts binary dependent variables. Both aim to model relationships between variables by estimating coefficients. Logistic regression models the log odds of the dependent variable rather than the variable directly. Key evaluation metrics for regression include accuracy, precision, recall, and F1 score, which are calculated using a confusion matrix.
Regression (Linear Regression and Logistic Regression) by Akanksha Bali
Regression analysis is a statistical technique used to examine relationships between variables. Linear regression finds the best fitting straight line through data points to model the relationship between a continuous dependent variable (Y) and one or more independent variables (X). Logistic regression produces results in a binary format to predict outcomes of categorical dependent variables. It transforms the linear equation using logarithms to restrict predicted Y values between 0 and 1.
Support vector machines (svm)
A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples. In two dimentional space this hyperplane is a line dividing a plane in two parts where in each class lay in either side.
Logistic regression with SPSS
Logistic regression is used to predict categorical outcomes. The presented document discusses logistic regression, including its objectives, assumptions, key terms, and an example application to predicting basketball match outcomes. Logistic regression uses maximum likelihood estimation to model the relationship between a binary dependent variable and independent variables. The document provides an illustrated example of conducting logistic regression in SPSS to predict match results based on variables like passes, rebounds, free throws, and blocks.
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This document provides an introduction to logistic regression. It outlines key features such as using a logistic function to model a binary dependent variable that can take on values of 0 or 1. Logistic regression is a linear method that uses the logistic function to transform predictions. The document discusses applications in machine learning, medical science, social science, and industry. It also provides details on logistic regression models, including converting linear variables to logistic variables using a sigmoid function and examining the effects of varying the logistic growth and midpoint parameters on the logistic regression curve.
Logistic regression in Machine Learning
Logistic regression is a predictive analysis algorithm that can be used for classification problems. It estimates the probabilities of different classes using the logistic function, which outputs values between 0 and 1. Logistic regression transforms its output using the sigmoid function to return a probability value. It is used for problems like email spam detection, fraud detection, and tumor classification. The independent variables should be independent of each other and the dependent variable must be categorical. Gradient descent is used to minimize the loss function and optimize the model parameters during training.
Feature selection
This document summarizes a machine learning workshop on feature selection. It discusses typical feature selection methods like single feature evaluation using metrics like mutual information and Gini indexing. It also covers subset selection techniques like sequential forward selection and sequential backward selection. Examples are provided showing how feature selection improves performance for logistic regression on large datasets with more features than samples. The document outlines the workshop agenda and provides details on when and why feature selection is important for machine learning models.
Linear regression analysis
This document provides an overview of linear regression analysis. It defines key terms like dependent and independent variables. It describes simple linear regression, which involves predicting a dependent variable based on a single independent variable. It covers techniques for linear regression including least squares estimation to calculate the slope and intercept of the regression line, the coefficient of determination (R2) to evaluate the model fit, and assumptions like independence and homoscedasticity of residuals. Hypothesis testing methods for the slope and correlation coefficient using the t-test and F-test are also summarized.
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The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
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1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modelling.
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Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
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Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
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Logistic regression in Machine Learning
Logistic regression is a predictive analysis algorithm that can be used for classification problems. It estimates the probabilities of different classes using the logistic function, which outputs values between 0 and 1. Logistic regression transforms its output using the sigmoid function to return a probability value. It is used for problems like email spam detection, fraud detection, and tumor classification. The independent variables should be independent of each other and the dependent variable must be categorical. Gradient descent is used to minimize the loss function and optimize the model parameters during training.
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This document summarizes a machine learning workshop on feature selection. It discusses typical feature selection methods like single feature evaluation using metrics like mutual information and Gini indexing. It also covers subset selection techniques like sequential forward selection and sequential backward selection. Examples are provided showing how feature selection improves performance for logistic regression on large datasets with more features than samples. The document outlines the workshop agenda and provides details on when and why feature selection is important for machine learning models.
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This document provides an overview of linear regression analysis. It defines key terms like dependent and independent variables. It describes simple linear regression, which involves predicting a dependent variable based on a single independent variable. It covers techniques for linear regression including least squares estimation to calculate the slope and intercept of the regression line, the coefficient of determination (R2) to evaluate the model fit, and assumptions like independence and homoscedasticity of residuals. Hypothesis testing methods for the slope and correlation coefficient using the t-test and F-test are also summarized.
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About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars. This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
By the end of this Machine Learning course, you will be able to:
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3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbours, K-means clustering and more.
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Suppose, you have a million samples in your dataset, so if you use a typical Gradient Descent optimization technique, you will have to use all of the one million samples for completing one iteration while performing the Gradient Descent, and it has to be done for every iteration until the minima are reached. Hence, it becomes computationally very expensive to perform.
This problem is solved by Stochastic Gradient Descent. In SGD, it uses only a single sample, i.e., a batch size of one, to perform each iteration. The sample is randomly shuffled and selected for performing the iteration.
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Support Vector Machine ppt presentation
Support vector machines (SVM) is a supervised machine learning algorithm used for both classification and regression problems. However, it is primarily used for classification. The goal of SVM is to create the best decision boundary, known as a hyperplane, that separates clusters of data points. It chooses extreme data points as support vectors to define the hyperplane. SVM is effective for problems that are not linearly separable by transforming them into higher dimensional spaces. It works well when there is a clear margin of separation between classes and is effective for high dimensional data. An example use case in Python is presented.
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This document provides an overview of clustering and classification techniques. It defines clustering as organizing objects into groups of similar objects and discusses common clustering algorithms like k-means and hierarchical clustering. It also provides examples of how k-means works and references for further information.
Linear Regression Analysis | Linear Regression in Python | Machine Learning A...
This Linear Regression in Machine Learning Presentation will help you understand the basics of Linear Regression algorithm - what is Linear Regression, why is it needed and how Simple Linear Regression works with solved examples, Linear regression analysis, applications of Linear Regression and Multiple Linear Regression model. At the end, we will implement a use case on profit estimation of companies using Linear Regression in Python. This Machine Learning presentation is ideal for beginners who want to understand Data Science algorithms as well as Machine Learning algorithms.
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1. Introduction to Machine Learning
2. Machine Learning Algorithms
3. Applications of Linear Regression
4. Understanding Linear Regression
5. Multiple Linear Regression
6. Use case - Profit estimation of companies
What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
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** Python Data Science Training : https://www.edureka.co/python **
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1. What is Regression?
2. What is Logistic Regression?
3. Why use Logistic Regression?
4. Linear vs Logistic Regression
5. Logistic Regression Use Cases
6. Logistic Regression Example Demo in Python
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Machine Learning (Classification Models)
Machine Learning (Classification Models)Makerere Unversity School of Public Health, Victoria University
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Module 3: Linear Regression
Delta Analytics is a 501(c)3 non-profit in the Bay Area. We believe that data is powerful, and that anybody should be able to harness it for change. Our teaching fellows partner with schools and organizations worldwide to work with students excited about the power of data to do good.
Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data.
To learn more about our mission or provide feedback, take a look at www.deltanalytics.org.
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Introduction to machine learning and model building using linear regression
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This document provides an overview of supervised machine learning algorithms, including linear regression, naive bayesian classification, and their applications. It discusses basic concepts like training a classification model on labeled data and testing it on new unlabeled data. Linear regression finds the best linear relationship between variables, while naive bayes assumes conditional independence between attributes. The document uses examples to illustrate classification of loan applications and text documents. It explains the mathematical process and advantages of the naive bayes approach.
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This document provides an overview of a machine learning course. It outlines the course prerequisites, description, learning outcomes, structure, grading breakdown, and topics to be covered. The course aims to develop practical machine learning and data science skills by covering theoretical concepts and applying them to programming assignments. It will be conducted online and involve lectures, assessments, a group project, and final exam. Key machine learning topics to be covered include supervised learning, unsupervised learning, and applications.
CHAPTER 11 LOGISTIC REGRESSION.pptx
This document discusses logistic regression, a classification algorithm used to predict the probability of discrete outcomes. It provides examples of classification problems like customer churn, credit risk, fraud detection. Logistic regression models the log odds of the dependent variable using the sigmoid function. The document outlines the steps to develop a logistic regression model using a default prediction dataset: preprocessing data, fitting a model on training data, interpreting coefficients, assessing fit, making predictions on test data, and evaluating the model's performance.
Machine Learning Course | Edureka
The document describes a machine learning certification training offered by Edureka. It covers topics like introduction to data science, machine learning applications, types of machine learning including supervised, unsupervised and reinforcement learning. For supervised learning, it discusses algorithms like linear regression, logistic regression, decision trees, random forest and Naive Bayes classifier. It also explains machine learning life cycle and concepts like model fitting, clustering and applications of machine learning.
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This document provides an overview of consumer credit risk modeling and scoring. It discusses various statistical methods used for credit scoring like logistic regression, neural networks, and support vector machines (SVM). For SVM, it describes how the optimal separating hyperplane is chosen to maximize the margin between different classes of data. It also discusses challenges in consumer lending and best practices for credit risk management.
Big Data Analytics.pptx
Supervised learning is a machine learning approach that's defined by its use of labeled datasets. These datasets are designed to train or “supervise” algorithms into classifying data or predicting outcomes accurately.
Machine learning algorithms and business use cases
A high level overview of machine learning algorithms and business use cases. Includes high level overview of statistical concepts for the algorithms.
Eco550 Assignment 1
1. A regression of price on lot size for 832 housing observations found that lot size was a statistically significant predictor of price, with an estimated slope parameter of 1.38850 (p<0.00001).
2. Tests for heteroskedasticity found evidence that the error variances were not constant, violating the homoskedasticity assumption.
3. Rerunning the regression with heteroskedasticity-robust standard errors produced larger standard errors compared to the original OLS standard errors, better accounting for the heteroskedasticity in the data.
Bivariate Regression
This document discusses bivariate linear regression and its understanding. Bivariate linear regression, also called simple linear regression, involves modeling the relationship between a dependent variable (Y) and a single independent variable (X). The regression equation takes the form of Y = β0 + β1X + ε, where β0 is the intercept, β1 is the slope coefficient, and ε is the error term. This equation can be used to predict Y values based on X values, as well as understand how much variation in Y can be explained by X. Parameters β0 and β1 are estimated to maximize the explanatory power of X for Y while minimizing prediction errors.
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YouTube Link: https://youtu.be/GJQ36pIYbic
DevOps Training: https://www.edureka.co/devops-certification-training
This Edureka DevOps Tutorial for Beginners talks about What is DevOps and how it works. You will learn about several DevOps tools (Git, Jenkins, Docker, Puppet, Ansible, Nagios) involved at different DevOps stages such as version control, continuous integration, continuous delivery, continuous deployment, continuous monitoring.
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みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
ここ3000字までしか入らないけどタイトルの方がたくさん文字入ると思います。
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- 3. DATA SCIENCE CERTIFICATION TRAINING www.edureka.co/data-science Types Of Machine Learning
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- 7. DATA SCIENCE CERTIFICATION TRAINING www.edureka.co/data-science Regression Vs Classification Classification Regression Classification is the task of predicting a discrete class label Regression is the task of predicting a continuous quantity • In a classification problem data is classified into one of two or more classes • A classification problem with two classes is called binary, more than two classes is called a multi-class classification Spam Non-Spam Mail • A regression problem requires the prediction of a quantity • A regression problem with multiple input variables is called a multivariate regression problem Predicted price Actual price Time Price
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