### What is Supervised Learning?

Learning from training data to make predictions about new data point is Supervised Learning.

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Learning from training data to make predictions about new data point is Supervised Learning.

Predicting a continuous numerical value (ex: wage, selling price, etc.) is Regression.

Assigning data into categorical compartments (ex: cat/dog, apple/pear/banana, etc.) is Classification.

Regression is primarily used when we are trying to model continuous numerical data. Classification is used when we are trying to predict categorical data.

Most of the concepts of binary classification transfer over to the situation of an outcome with more than two levels, which is referred to as multi-class classification.

Both discriminative and generative models have the ability to learn model parameters in a classification setting, but they are based on entirely different mechanisms and serve different purposes.

Linear models are a class of models in which a response variable is linearly related to one or more predictors.

Logistic regression is the most traditional classification algorithm and preserves many of the advantages in interpretation as linear regression for a continuous outcome.

A weak learner refers to a prediction mechanism that produces results that are only slightly more predictive than those resulting from a random chance model.

If the overall distribution of the outcome is heavily tilted towards one class compared to the other, the classification problem is considered to be imbalanced.

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