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Grokking Modern System Design Interview for Engineers & Managers

Ace your System Design Interview and take your career to the next level. Learn to handle the design of applications like Netflix, Quora, Facebook, Uber, and many more in a 45-min interview. Learn the RESHADED framework for architecting web-scale applications by determining requirements, constraints, and assumptions before diving into a step-by-step design process.

**Regression** and **classification** are two techniques used when designing *machine learning algorithms*. Both regression machine learning algorithms and classification machine learning algorithms are classified under the realm of *supervised machine learning*.

Supervised machine learning occurs when a model is trained on existing data that is correctly labeled.

The key difference between classification and regression is that classification predicts a discrete label, while regression predicts a continuous quantity or value.

Let’s consider regression and classification individually:

Regression is the process of finding a model that predicts a continuous value based on its input variables. In regression problems, the goal is to mathematically estimate a mapping function$(f)$ from the input variables$(x)$ to the output variables$(y)$.

Consider a dataset that contains information about all the students in a university. An example of a regression task would be to predict the height of any student based on their gender, weight, major, and diet. We can do this because height is a continuous quantity; i.e., there are an infinite amount of possible values for a person’s height.

A regression algorithm is commonly evaluated by calculating the *root mean squared error* of its output.

On the other hand, classification is the process of finding a model that separates input data into multiple discrete classes or labels. In other words, a classification problem determines whether or not an input value can be part of a pre-identified group.

Consider the same dataset of all the students at a university. A classification task would be to use parameters, such as a student’s weight, major, and diet, to determine whether they fall into the “Above Average” or “Below Average” category. Note that there are only two discrete labels in which the data is classified.

A classification algorithm is evaluated by computing the *accuracy* with which it correctly classified its input.

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machine learning

regression

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Grokking Modern System Design Interview for Engineers & Managers

Ace your System Design Interview and take your career to the next level. Learn to handle the design of applications like Netflix, Quora, Facebook, Uber, and many more in a 45-min interview. Learn the RESHADED framework for architecting web-scale applications by determining requirements, constraints, and assumptions before diving into a step-by-step design process.

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