Before answering the question of why is correlation used in machine learning, let us first understand **what is correlation in machine learning** then we will later dive into why it is used.

## Table of Contents

## What is Correlation in Machine Learning?

Correlation in machine learning is a technique, precisely a statistical technique by which we can learn how one or more variable components influence each other. In simple words, we can learn how different variables change with respect to other variables in data. It is one of the most important and commonly used approaches to learning more insights about data. Data Scientists and Analysts across the domain use the Correlation technique in machine learning for exploratory analysis.

It is important to understand that a high correlation score between 2 variables tells us that those 2 variables highly influence each other and are closely related whereas, a low correlation score guides us in learning that those 2 variables do not move much concerning each other hence they are loosely related to each other.

With the help of correlation technique in machine learning you can determine patterns and structure of data in order to produce insights that can be significant for research purposes. Correlation helps us answer questions where the relationship between two items is important to understand such as does higher screen time leads to an increase in mental fatigue and questions like that.

There are different types of Correlation in machine learning:

**Positive Correlation – **Correlation of two variables `a`

and `b`

is said to be positive when an increase in the values of the variable `a`

leads to an increase in the values of the variable `b`

. There’s a positive linear relationship between `a`

and `b`

. Below is a graph demonstrating the same.

**Negative Correlation** – Correlation of two variables `a`

and `b`

is said to be positive when an increase in the values of the variable `a`

leads to a decrease in the values of the variable `b`

. There’s a negative linear relationship between `a`

and `b`

. Here is a graph displaying the same.

**Neutral Correlation** – A Neutral Correlation is said to be in action when there is no solid change relationship in the values of variables `a`

and `b`

with respect to each other.

## Measuring Correlation

Several methods are commonly used to measure the degree of correlation between variables in machine learning. Two of the most popular methods are:

### Pearson’s correlation coefficient (*r*)

Pearson’s correlation coefficient is a score that measures the linear correlation between two variables. Pearson’s correlation coefficient is represented by *r*. To calculate Pearson’s Correlation Coefficient we divide the covariance of variables x and y by the product of each variable’s standard deviation.

The value of the Pearson Coefficient ranges from -1 to +1, where the value of +1 signifies that those two variables have a strong positive collinearity, while a score of -1 indicates that they have a strong negative relationship with each other and a value of 0 indicates no correlation between the variables. It is widely used in machine learning to understand the linear relationship between features and the target variable.

### Spearman’s Rank Correlation Coefficient (*ρ*)

The problem with Pearson’s correlation coefficient is that it assumes that variables possess a linear relationship between them. To tackle this, Spearman’s Coefficient is proposed which assumes that the relationship between variables is not linear but monotonic. Monotonic Relationship refers to the relationship where the value of one variable could either decrease or increase while the other variable increases, it’s monotonic.

Spearman’s Coefficient is useful when dealing with non-linear or ordinal data, whereas Pearson’s coefficient is useful when dealing with linear data. Like Pearson’s Coefficient, the values of Spearman’s Coefficient also lie in the range of -1 to 1 (-1 being a strongly negative relationship while 1 being a strongly positive relationship). It is represented by rho (ρ). Learn more about Spearman’s Coefficient.

**Also Read: **Differences Between Supervised and Unsupervised learning in machine learning

## Why is Correlation used in Machine Learning?

These are the following reasons why is correlation used in machine learning:

**Feature Selection and Engineering**: One of the most important roles that correlation plays in machine learning is in feature selection and engineering. Let’s say, you have 50 features in your dataset and you might think that it’ll make your model training a little complex, so what you can do is only consider features that influence more than the other features. In this case, you can use collinearity in order to see which features out of 50 are influencing more, so you can consider only those features where the r score is more than 0.50 and less than -0.50. That’s how feature selection is performed with the help of correlation, by doing this we can improve our model performance and reduce complexity at the same time.**Anomaly Detection:**In anomaly detection tasks, we can use correlation to identify unusual patterns in data. The correlation between different data points can be considered to flag anomalies or outliers in the dataset. It is beneficial in cybersecurity and fraud detection, where detecting irregular behavior is paramount.**Data Preprocessing:**You might be familiar that before feeding data into machine learning algorithms, it often requires preprocessing and one of the steps in preprocessing is handling missing values. Here, Correlation can help us impute missing values by looking at the relationships between variables. If two variables are highly correlated, we can use one to predict and fill in the missing values of the other.**Multicollinearity Detection:**Multicollinearity occurs when two or more independent variables in a dataset are highly correlated with each other. This poses a significant problem in regression analysis, as it makes it challenging to identify the individual impact of each variable on the dependent variable. This problem can also be tackled using Correlation, we can detect multicollinearity as a result of which we can either remove one of the correlated variables or take corrective actions to mitigate its effects on the model.

## Conclusion

To conclude, Correlation is a statistical technique that displays the strength of the relationship between two variables and how they change with respect to each other. In simpler terms, it helps us determine whether and how two sets of data are related to each other. We answered the question of why is correlation used in machine learning, the reason being better feature selection and engineering, for anomaly detection, data preprocessing, and multicollinearity detection.

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