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Deep Learning A-Z™: Regression & Classification - Module 7

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Kirill Eremenko

The document discusses different types of regression models: - Simple linear regression predicts a dependent variable from a single independent variable. - Multiple linear regression predicts a dependent variable from multiple independent variables. - Logistic regression predicts a binary dependent variable (e.g. yes/no) from independent variables by modeling the probability.

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© SuperDataScienceDeep Learning A-Z
© SuperDataScienceDeep Learning A-Z
© SuperDataScienceDeep Learning A-Z Simple Linear Regression y = b0 + b1*x1 Dependent variable (DV) Independent variable (IV) CoefficientConstant
© SuperDataScienceDeep Learning A-Z Simple Linear Regression: Salary ($) Experience y = b0 + b1*x Salary = b0 + b1 *Experience +1yr +10k 30k
© SuperDataScienceDeep Learning A-Z
© SuperDataScienceDeep Learning A-Z Simple Linear Regression: Salary ($) Experience yî yi SUM (y - ŷ )2 -> min
© SuperDataScienceDeep Learning A-Z
© SuperDataScienceDeep Learning A-Z Simple Linear Regression Multiple Linear Regression y = b0 + b1*x1 y = b0 + b1*x1 + b2*x2 + … + bn*xn Dependent variable (DV) Independent variables (IVs) Constant Coefficients
© SuperDataScienceDeep Learning A-Z
© SuperDataScienceDeep Learning A-Z y = b0 + b1*x1 + … + bn*xn Linear Regression: y = b0 + b1*x - Simple: - Multiple:
© SuperDataScienceDeep Learning A-Z We know this:This is new: Salary ($) Experience y = b0 + b1*x?? ? Action (Y/N) Age 0 1 -
© SuperDataScienceDeep Learning A-Z Action (Y/N) Age 0 1 -
© SuperDataScienceDeep Learning A-Z 1 - Action (Y/N) Age 0
© SuperDataScienceDeep Learning A-Z 1 - Action (Y/N) Age 0
© SuperDataScienceDeep Learning A-Z y = b0 + b1*x 1 + e-y 1 p = ln ( ) = b0 + b1*x1 – p p
© SuperDataScienceDeep Learning A-Z
© SuperDataScienceDeep Learning A-Z ln ( ) = b0 + b1*x1 – p p y (Actual DV) X p̂ (Probability) p_hat
© SuperDataScienceDeep Learning A-Z X p̂ (Probability) 20 30 40 50 p̂ =0.7% p̂ =23% p̂ =85% p̂ =99.4% p_hat
© SuperDataScienceDeep Learning A-Z X p̂ (Probability)y (Actual DV) ŷ (Predicted DV) 0.5 ŷ = 0 ŷ = 0 ŷ = 1 ŷ = 1 1 Fin.

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