For a linear regression model, the following data is obtained. x -1.34 -0.45 0.45 1.34 where Ypred = WTx + b is the prediction model. 1. Initialize W = 0.1, b = 0.2, Take learning rate a = 0.01 and apply Gradient Descent algorithm (for one single iteration) to obtain the next value of W = ? and b = ?, y 2 4 6 8 Y pred 0.08 1.02 1.95 2.89 2. Determine Mean Square Error (MSE) for the data given in the table.
For a linear regression model, the following data is obtained. x -1.34 -0.45 0.45 1.34 where Ypred = WTx + b is the prediction model. 1. Initialize W = 0.1, b = 0.2, Take learning rate a = 0.01 and apply Gradient Descent algorithm (for one single iteration) to obtain the next value of W = ? and b = ?, y 2 4 6 8 Y pred 0.08 1.02 1.95 2.89 2. Determine Mean Square Error (MSE) for the data given in the table.
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Transcribed Image Text:For a linear regression model, the following data is obtained.
x
-1.34
-0.45
0.45
1.34
y
2
4
6
8
Y Pred
0.08
1.02
1.95
2.89
where Ypred = WT x + b is the prediction model.
1. Initialize W = 0.1, b = 0.2, Take learning rate a = 0.01 and apply Gradient
Descent algorithm (for one single iteration) to obtain the next value of W = ? and b = ?,
2. Determine Mean Square Error (MSE) for the data given in the table.
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