What is Regression ?

Explore the concept of regression in supervised machine learning, focusing on predicting real-valued outputs. Understand essential terms like independent features, dependent outputs, and dataset types. Learn about numerical and categorical features, and grasp important issues like overfitting and underfitting to build effective predictive models.

We'll cover the following...

- What is Regression?
- Key terms
- - Overfitting and underfitting

What is Regression?

Regression comes under supervised learning and it involves predicting a real-valued output. Classification predicts 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

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8.Deep Learning

9.Machine Learning Tools and Libraries

10.Big Data Tools and Technologies

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