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Heteroskedasticity vs. Homoskedasticity ----> Assumption of Linear Reg

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Linear Regression model should be validated for all model assumptions including the definition of the functional form. If the assumptions are violated, we need to revisit the model.Since Linear Regression is the quantitative analysis we need to validate some assumption about the data and the predicted model. Assumption on Data before Training the Model :  Multicollinearity,Linear Relationship,No Hidden Value Assumption on Model after Training the Model :  Normality of Residuals,Homoscedasticity Lets Discuss Homoscedasticity vs Heteroskedasticity Homoscedasticity (homo — equal , scedasticity — spread): Homoscedasticity in a model means that the error is constant along the values of the dependent variable and refers to situations where the residuals are equal across all the dependent variables.If a model is homoskedastic, we can assume that the residuals are drawn from a population with constant variance. It would satisfy one of the assumptions of the OLS regression and ensure t...
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Why Linear Regression is of 3 Types ?

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Since all Linear Regressions are used to find the relationship between the continuous variable ,what are all difference between them and Let's see their use cases. Simple Linear Regression : The ML Algorithm which mainly used for Continuous Data,to predict the relationship between  dependent variable and one independent variable The core idea is to obtain a line that best fits the data. The best fit line is the one for which total prediction error (all data points) are as small as possible. Prediction or Target output is always a Continuous value for all Linear Regression Types. Y=bx+c Let's Slope be b, If b > 0, then x(predictor) and y(target) have a positive relationship . That is increase in x will increase y.  If b < 0, then x(predictor) and y(target) have a negative relationship . That is increase in x will decrease y. Features: Always Linear.It should contain only one independent variable Multivariate Regression: There is a target variable which is dependent on mul...
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Machine Learning- It's Not Just Algorithms===>The Beginner questions !!!

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Let's Start with AI... In computer science the term artificial intelligence has played a very prominent role. The term has become more popular due to recent advances in Artificial Intelligence and Machine Learning. One of the finest example is ChatGPT ,machine started to give solution to many of the human problems.But how it is happening? Because Machine started to find the patterns inside the give data Machine learning is the area of artificial intelligence where machines are responsible for completing daily tasks and are believed to be smarter than humans. They are known to learn, adapt and perform much faster than humans and are programmed to do so. Robotics and integration with IoT devices have taken machines to think and work to a new level where they out-perform humans in their cognitive abilities.The vast amount of data generation from multiple sources everyday is also one of the reason for these AI Evolution. What is Machine Learning? Is it the set of Algorithms or neural n...
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