Multivariate Linear Regression: Concepts and Implementation

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Multi Variant Linear Regression

####Linear regression with multiple variables is also known as "multivariate linear regression".

Suppose y is function of multiple variables x then

X_column^(row) : X(i, j)

• m = number of training example
• n = number of feature in training example
• x⁽ⁱ⁾ = i^th row in training example/ feature in example
• x_j⁽ⁱ⁾ = i^th row , j^th column in training example

Linear Regression for Hypothesis for n features :

h_Θ(x) = Θ₀X0 +Θ₁X1 +Θ₁ X2+ Θ₁ X3+ ...Θ_nXn

Assume X0= 1 for convenience

h_Θ(x) = Θ₀X0 +Θ₁X1 +Θ₁ X2+ Θ₁ X3+ ...Θ_nXn

h_Θ(x) = Θ^TX

Inner Product of RowVector0(1n) Column VectorX(n1) = Scalar()11 Hypothesis

Notes:

• Θ^T is a 1 by (n+1) matrix and not an (n+1) by 1 matrix
• x₀⁽ⁱ⁾ =1 for matrix multiplication

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Simple Linear Regression

Cost function:

   J(Theta0, Theta1) =  1/2m(Sum((predicted-actual)**2))              
                     =  1/2m(Sum((h(xi)-y(i))**2)) 
                     =  1/2number of dataSet(Sum of Deviation from actual)**Squared to remove negative
            

Gradient Descent:

Steps:

• For feature index j= 0,1, repeat until convergence

  

• Simultaneous compute Theta(0), Theta (1) and store in temp values

• Simultaneous Update Theta(0), Theta (1)

Multi variant Linear Regression

Cost Function

  J(Theta0, Theta1, Theta.....Thetam) =  1/2m(Sum((predicted-actual)**2))              
                         =  1/2m(Sum((h(xi)-y(i))**2)) 
                         =  1/2number of dataSet(Sum of Deviation from actual)**Squared to remove negative
                

J(Theta) = 1/2m(Sum((predicted-actual)**2))

Gradient Descent:

Steps:

• For feature index j= 0,1,....n repeat until convergence

  

• Simultaneous compute Theta(0), Theta (1), .... Theta (n) and store in temp values

• Simultaneous Update Theta(0), Theta (1) .... Theta (n)

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Feature scaling

Make sure feature are on same scale other wise contour will be skew elliptical(2000/5). Gradient descent on skew Eclipse take long time to reach local minima

xi = (xi--min(X))/(max(X)-min(X))

• Try to get feature into -1<= xi<= 1. Long gape will not fully scaled. Idellay should be withing range -3 to +3.

Feature scaling is way to avoid creating skew ellipse:

Mean Normalization

Use mean and max range to normalize x values

xi = (xi-Avg(X))/(max(X)-min(X))

xi = (xi-Avg(X))/(max(X)-min(X))

Debugging gradient descent using Learning Rate

plot the cost function, J(θ) over the number of iterations of gradient descent. If J(θ) ever increases, then you probably need to decrease α.

• If α is too small: slow convergence.

• If α is too large: may not decrease on every iteration and thus may not converge.

  

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Polynomial Regression

Sometimes by defining a new feature you might get a better Model that requires less computation

for example house price can defined by calculating area instead of creating 2 variable equation we can define one variable equation :

• Sometimes prediction fits Polynomial equation instead of linear equation
• Scaling of feature becomes crucial in Polynomial Regression
• Some algo can choose feature to fit polynomial curves