Before answering the question of why correlation is used in machine learning, let’s first understand **what is correlation in machine learning** we will see later why it is used.

## 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. Simply put, we can learn how different variables change relative to other variables in the data. This is one of the most important and commonly used approaches to gain more insights from data. Data scientists and domain analysts 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 these 2 variables strongly influence each other and are closely linked while a low correlation score guides us in learning that these 2 variables do not move very much in relation to each other, so they are loosely related to each other.

Using the correlation technique in machine learning, you can determine patterns and structure of data to produce information that may be important for research purposes. Correlation helps us answer questions where it is important to understand the relationship between two things, for example, does higher screen time lead to increased 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 is a positive linear relationship between `a`

And `b`

. Below is a chart 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 is a negative linear relationship between `a`

And `b`

. Here is a chart showing the same thing.

**Neutral correlation** – A neutral correlation is said to be in action when there is no strong change relationship in the values of the variables. `a`

And `b`

one to another.

## Measure 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 correlation coefficient (*r*)

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

The value of the Pearson coefficient ranges from -1 to +1, where the value of +1 means that these 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 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 the Pearson correlation coefficient is that it assumes that the variables have a linear relationship between them. To solve this problem, Spearman’s coefficient is proposed, which assumes that the relationship between variables is not linear but monotonic. Monotonic relationship refers to the relationship in which the value of one variable may decrease or increase while the other variable increases, it is monotonic.

The Spearman coefficient is useful when dealing with non-linear or ordinal data, while the Pearson coefficient is useful when dealing with linear data. Like Pearson’s coefficient, Spearman’s coefficient values also fall 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 coefficient.

**Read also: **Differences Between Supervised and Unsupervised Learning in Machine Learning

## Why is correlation used in machine learning?

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

**Feature selection and engineering**: One of the most important roles correlation plays in machine learning is feature selection and engineering. Let’s say you have 50 features in your dataset and you might think that this will make training your model a bit complex, so you can only consider the features that influence more than other features. In this case, you can use collinearity in order to see which features out of 50 influence the most, so that you can only consider features whose r-score is greater than 0.50 and less than -0.50. This is how feature selection is done using correlation, which allows us to improve the performance of our model and reduce complexity at the same time.**Anomaly detection:**In anomaly detection tasks, we can use correlation to identify unusual patterns in the data. Correlation between different data points can be seen as signaling anomalies or outliers in the data set. It is beneficial in cybersecurity and fraud detection, where detecting irregular behavior is paramount.**Data preprocessing:**You may know that before feeding data into machine learning algorithms, it often requires preprocessing and one of the steps of preprocessing is to handle missing values. Here, correlation can help us impute missing values by looking at 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 data set are highly correlated with each other. This poses a significant problem in regression analysis because it makes it difficult to identify the individual impact of each variable on the dependent variable. This problem can also be solved using correlation, we can detect multicollinearity through which we can either remove any of the correlated variables or take corrective action 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 in relation to each other. In simpler terms, it helps us determine if and how two sets of data are related to each other. We answered the question of why correlation is used in machine learning, the reason being better feature selection and engineering, for anomaly detection, data preprocessing and multicollinearity detection.

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