The process allows the classification and categorizing of data points into specific groups. The points, clustered into specific groups, have similarities in their features and properties & are distinct from the ones in other clusters.

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What is linear regression in machine learning?

Ans.: Linear regression is the most well-known supervised learning technique in machine learning. It is exceedingly simple and uses a linear relationship between an explanatory and a response variable to forecast and predict behavior.

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The equation for linear regression is the equation of a straight line à y = a + bx

Where x is the independent or predictor variable, y is the predicted or dependent variable, and a & b define the slope & intercept of the line and are the model parameters.

Parameters vs. Hyperparameters: How Do They Differ?

Ans.: Hyperparameters are standard parameters that work under all circumstances. Unlike model parameters, these are essential features or external configuration variables whose values cannot be ascertained through training data.

Hyperparameters are employed to determine model parameters. Tuning a machine learning algorithm for a specific problem allows one to discover the best-fit model parameters necessary to make accurate predictions.

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