Regression vs Classification vs Clustering

Martin James

6.1k

I a preparing for an interview. My question is about the differences between regression, classification and clustering and to give an example for each.
Can someone help me ?

According to Microsoft Documentation :
Regression is a form of machine learning that is used to predict a digital label based on the functionality of an item. For example, suppose Adventure Works Bikes is a business that rents bikes in a city. The company could use historical data for an older model that predicts daily locate demand to make sure enough staff and bikes are available.

Classification is a form of machine learning used to predict what category, or class, an item belongs to. For example, a clinic can use a patient’s characteristics (such as age, weight, blood pressure, etc.) to predict whether the patient is at risk for diabetes. In this case, the patient’s characteristics are traits, and the label is a classification of 0 or 1, representing non-diabetic or diabetic.

Clustering is a form (non-supervised) of machine learning used to group items into clusters or clusters based on the similarities in their functionality. For example, a botanist can measure plants and group them based on similarities in their proportions.

Amira Bedhiafi

It sounds like you're gearing up for an important interview! To clarify: regression predicts continuous outcomes (like estimating house prices), classification assigns items to categories (like email spam detection), and clustering groups similar data points (think customer segmentation). If you're looking for some engaging ways to practice, you might enjoy exploring resources related to data analysis, like the interesting simulations at [Monkey Mart](https://monkeymartfree.com). Good luck with your preparation!

Monkey Mart

Nov 10

The core differences between Regression, Classification, and Clustering lie in their output type and their use of labeled data. Regression: This is a supervised learning task that predicts a continuous numerical value. The goal is to answer "How much?" or "How many?". Example: Predicting a house's price based on features like its size and location. Classification: Also a supervised learning task, classification predicts a discrete category or class. It answers "Which category?" or "Is this A or B?". Example: Classifying an email as "spam" or "not spam". Clustering: This is an unsupervised learning task, meaning it works with unlabeled data. Its goal is to discover natural groups or clusters within the data. Example: Customer segmentation, where you group customers based on purchasing behavior to find distinct marketing groups, without any predefined labels. In summary: Regression and Classification are supervised tasks that predict an output (a number vs. a category), while Clustering is an unsupervised task that discovers hidden structures in data. If you are under a lot of pressure, you can play shleep at night to fall asleep

CyrusJasper

Oct 29

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Ke Wendell

Sep 27

Thanks you.

Chaya Anderson

Mar 21

Regression: Regression is a statistical method for estimating future values from given sets of data. For instance, one could use it to forecast the price of a home by looking at its square footage, number of bedrooms, and neighborhood. Classification: Classification is a way to sort information into different categories. For instance, one could use it to determine whether an email is spam or not by looking at its subject line and body. Clustering: Clustering is a way to group similar pieces of information into larger sets. For instance, one could use it to categorize customers according to their buying habits.

Phillip Barnes

There’s a lot of information here that can help any business start a social networking campaign that works age of war

kiricowell

a very good interview question distinguishing Regression vs classification and clustering. I can answer this question as follows. Regression and Classification are types of supervised learning algorithms while Clustering is a type of unsupervised algorithm. When the output variable is continuous, then it is a regression problem whereas when it contains discrete values, it is a classification problem.

Edward Henderson

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