What Is Regression ?

Explore the concept of regression within supervised machine learning. This lesson helps you understand how real-valued outputs are predicted using independent and dependent features, the roles of training, test, and validation datasets, and the differences between numerical and categorical data. You'll also learn about common challenges such as overfitting and underfitting to better grasp model performance.

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What is regression?
Key terms

What is regression?

Regression comes under supervised learning and involves predicting a real-valued output, while classification involves predicting a discrete-valued output.

Key terms

Input column or independent features

The columns that are used to predict the output column are called the input columns or independent features. These are denoted as $x_1$ , $x_2$ , $x_3$ , … $x_n$ where $x_1$ is the first feature and so on. Note that $n$ denotes the total number of features or dimensions.

Output column or dependent feature

The column that is to be predicted is called the output column or dependent feature. It is denoted as $y$ ...

1.What Is Data Science ?

2.Applications of Data Science

3.Overview of Libraries

4.Probability and Statistics

5.Machine Learning Part-1

6.Machine Learning Part-2

7.Machine Learning Part-3

8.Deep Learning

9.Machine Learning Tools and Libraries

10.Big Data Tools and Technologies

11.Where to go next ?

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