Supervised and Unsupervised Learning Supervised and Unsupervised Learning

Supervised and Unsupervised Learning

  DataScience[Updated on:Jun-26-2022]      |  Reading Time: About 3 minutes

Create a model which predicts handsomeness?

Name Age Handsome?
Ashwin 24 Yes
Dev 22 Yes
Karthik 28 Yes
Mohan 32 No

For every model there should be a target variable and features.

One must know what is supervised and unsupervised learnings.

Supervised Learning:

The machine learns from the labled dta, ie., we already know the reult of the input data.

it is called supervised learning because the algorithm learns from a data set

Problem statement:

Create a model and categorize into groups?

For unsupervised learning, the data would be categorized into groups.



Is the process of grouping things according to the similar features they share. Categorizing the data with respect to their features. 

What is regression?

Linear regression is a widely used for predictive analysis.  The idea of regression is to examine two things:

(1) does a set of predictor variables do a good job in predicting an outcome (dependent) variable?

 (2) Which variables in particular are significant predictors of the outcome variable, and in what way do they–indicated by the magnitude and sign of the beta estimates–impact the outcome variable?

 These regression estimates are used to explain the relationship between one dependent variable and one or more independent variables.  The simplest form of the regression equation with one dependent and one independent variable is defined by the formula y = mx+c, where y = estimated dependent variable score, c = constant, m = regression coefficient, and x = score on the independent variable.

Naming the Variables.  There are many names for a regression’s dependent variable.  It may be called an outcome variable, criterion variable, endogenous variable, or regressand.  The independent variables can be called exogenous variables, predictor variables, or regressors.

Three major uses for regression analysis are (1) determining the strength of predictors, (2) forecasting an effect, and (3) trend forecasting.

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