A lot of permutation and combination is required while interpreting data with the help of technologies such as Machine Learning and Artificial Intelligence. It helps to predict different outcomes in different situations. These permutations and combinations are done widely with the help of variables.
A variable is an item, quantity, or number that can be measured in a data set. This item or quantity is not fixed and can change in different situations through internal or external factors. Whenever the exact value of an element is unknown, we term it as a variable. Therefore, variables are often referred to as placeholders of an unknown quantity. A variable is defined as a changeable value that depends on a computer program’s commands or input in computer and programming language.
For instance, in an eCommerce data set that comprises customer purchases, customer preferences or likeliness to order a particular product in the future is a variable. It depends on customers’ needs, income, age, and other factors. Let us learn more about how variables function and how the relationship between two variables is determined.
What is Covariance?
Covariance measures the relation or mutual dependency between two variables. It determines in what direction a variable would move if the other variable changes the same data set.
Covariance is of two types – positive and negative. When one variable is altered and the second variable moves in the same direction, it is called positive covariance. If the second variable moves in the opposite direction, it is termed as negative covariance.
The higher value of covariance signifies the dependency of the two variables. A positive covariance means that the variables are directly proportional and will move in the same direction. Negative covariance values tell us that the two random variables are indirectly related and move in opposite directions. It means if the quantity of one variable increases, that of another will decrease.
What is Correlation?
There can be more than one variable in a given situation or dataset. These variables may either be entirely related or unrelated to each other. It is crucial to derive the relationship between two variables to maintain accuracy while figuring out favourable outcomes. This is known as correlation, a statistical measure denoting the relation between two variables.
Correlation explains the linear relationship between two variables and showcases the movement of one variable when the other variable changes.
If there are two variables X and Y, and there is a change in X, the correlation would measure the change in Y due to a variation in X. It calculates whether Y will show a positive change or a negative change with a change in X.
Similar to covariance, there are three types of correlation – positive, negative, and zero. In positive correlation, when one variable goes up on a graph, the other variable also goes up. In a negative correlation, if one variable goes up, the other variable goes down. In a positive and negative correlation, the variables go up and down proportionally or linearly. If the correlation is zero, then the variables are unrelated, and there is no linear graph.
Correlation is measured with the help of the Pearson coefficient. The value of the coefficient of correlation ranges between -1 to 1.
What is the Difference between Covariance and Correlation?
The terms covariance and correlation are often used interchangeably. However, they are not the same. It is difficult for people to figure out the differences between the two. Let us understand in detail covariance vs correlation.
1. What it measures
Covariance and correlation are very similar and confusing. Both are a measurement of variables. However, a remarkable difference between the two is that covariance measures the change between the variables. It tells how two variables are related to each other and whether they move in the same or opposite directions. Covariance does not define how the variables change. It just confirms whether the variables are related to each other or not.
On the other hand, The extent or degree of change of variables is determined with the help of correlation. It is a function of covariance.
The value of correlation ranges between -1 to 1. On the other hand, the value of covariance can be any number. Its value falls between the negative power and the positive power of infinity. Correlation has standardized values whereas, the importance of covariance is not definite. We can derive the value of correlation if we know the coefficient of covariance.
3. Change of Scale
It means the output change when the variables are multiplied by a constant value. The shift in scale does not affect the value of correlation. Even if the variables are multiplied by a constant, the correlation will remain the same. However, this is not in the case of covariance. It is affected by a change of scale. If the variables are multiplied to a constant, the covariance will change accordingly.
What is the Use of Covariance and Correlation in Machine Learning?
There are various variables in machine learning – target, independent, moderate, confounding, and control variables. These variables perform different functions and play a vital role in ML algorithms and techniques. The primary job of variables is to add missing values in algorithms. Since data is not always available in a structured form, there can be missing elements. Algorithms cannot function with incomplete information. Therefore, software engineers or developers use variables in machine learning to fill the missing values.
What are the Career Opportunities in Machine Learning?
One of the crucial components of machine learning and AI technologies is the dependency of variables or the relationship between two random variables. ML and AI algorithms deliver results by identifying the relationships between two variables. Therefore, if you want to build a career in machine learning, you must know the covariance and correlation concepts.
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Variables play a crucial role in data analysis and decision-making in machine learning and artificial intelligence. Correlation and covariance help to determine whether or not a relationship exists between two variables. Companies can then predict desired outcomes and make decisions accordingly. It is one of the most complex yet crucial concepts of machine learning. To gain expert knowledge on Machine Learning and AI, you can pursue upGrad’s program on ML and AI.
Are covariance and correlation the same thing?
No, covariance and correlation are not the same concepts, although they are closely related. Covariance determines the linear relationship between two random variables, and correlation measures the degree of relation between the two variables.
Are covariance and correlation relevant in machine learning?
Yes, covariance and correlation are essential in machine learning as the ML algorithm interprets the relationship between variables and delivers output accordingly. Therefore, you must learn about covariance and correlation.
What skills are necessary for a career in Machine Learning and AI?
To secure a job in ML or AI industries, you should be adept at data analysis, probability and statistics, computer programming, software development, linear regression, signal processing, Natural Language Processing, and more.