Page 275 - Artificial Intellegence_v2.0_Class_11
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UNIT-8
REGRESSION
Learning Outcomes
• Regression • Crosstabs
• Scatterplots • Regression—Finding the Line
• Regression—How good is the Line? • Regression—Describing the Line
• Correlation • Pearson's r—Correlation Coefficient
• Importance of data in Regression Analysis • How Prediction Changes with Changing Data?
• Correlation is not Causation
Machine Learning/Artificial Intelligence has become an integral part of our society. It is used in various industries to get more
accurate results and to have better control of the future. For example, machine learning helps farmers to make accurate
prediction and estimation of farming parameters, educationists using machine learning to automate the grading and
assessment of activities like multiple choice questions, bankers using AI to track financial transactions and analyse user data,
scientists using AI to make more accurate short-term predictions, including for critical storms and floods.
In this unit, you will learn about regression and correlation. You will also learn about the Pearson correlation coefficient
(r) measures and characteristics of the Pearson.
Regression
As we know that most Machine Learning algorithms use supervised learning. There are mainly two types of supervised
learning algorithms which are regression and classification. You will learn about classification in next unit.
Regression is a Machine Learning algorithm used to analyse the relationship among dependent (target) and independent
(predictor) variables. It predicts the output values based on input values. It is manly used for weather forecasting, finding the
causal-effect relationship between variables and time series modelling.
In regression tasks, there are two kinds of variables being studied: the dependent variables and the independent variables.
• Independent variables: Quantities that can be measured directly.
• Dependent variables: Quantities for which value depends on independent variables.
As the independent variable is adjusted, the level of the dependent variable will vary. The dependent variable is the variable
under study, and it is the variable that the regression model tries to predict. In the linear regression task, each observation is
made up of the value of the dependent variable and the value of the independent variable.
Regression 273

