Why Machine Learning
Explore why traditional regression models have limitations and understand the role of machine learning in handling complex data relationships. Learn the basics of supervised and unsupervised learning and how machine learning differs from AI and data science to improve predictive analysis.
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Issues with Regression
In the previous chapter, we learned how to use linear and logistic regression models for making predictions from data. But there are some issues with the regression framework. We will look at these issues one by one.
Non-linear relationships
By design, linear regression explores linear relationships between the dependent and independent variables. It assumes that there is a straight-line relationship between the variables and tries to find the line that best fits the data. Sometimes, it is not the case that variables follow a linear relationship. For instance, the relationship between age and income is not linear. Income rises exponentially during the early years and then grows almost linearly at the later stages.
Linear Regression parameters converge on the mean of the predicted variable
Linear ...