Hello World Of Ml
Linear Regression
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Regression is used to determine which variables have impact on a topic of interest
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Linearity is mathematical representation of relationship between variables as a line
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Regression allows us to determine which factors matter most, which factors can be ignored, and how these factors influence each other.
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Core idea is to obtain BEST FIT Line
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Best fit line has the total prediction error is as small as possible
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Error is the distance between data point to the Regression line
Source:www.scribbr.com
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Simple LR- One dependent variable, one independent variable
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Multiple LR- One dependent variable, multiple independent variables
Source:www.sthda.com
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y is the predicted value for any given value of the independent variable (x)
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B0 is the intercept, the predicted value of y when the x is 0
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B1 is the regression coefficient – how much we expect y to change as x increases
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x is the independent variable
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e is the error of the estimate, or how much variation there is in our estimate of the regression coefficient
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Goal is to find best fit line by searching for the regression coefficient (B1) that minimizes the total error (e) of the model
LR Metric-MSE(Mean Squared Error) and it is calculated by
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measuring the distance of the observed y-values from the predicted y-values at each value of x
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squaring each of these distances
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calculating the mean of each of the squared distances
Real world applications of LR
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Effect of different training regimens have on player performance
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effect of fertilizer and water on crop yields