# PPT

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Machine Learning and Data Mining
Linear regression
Supervised learning
• Notation
–
–
–
–
Features
x
Targets
y
Predictions ŷ
Parameters q
Learning algorithm
Program (“Learner”)
Training data
(examples)
Features
Feedback /
Target values
Characterized by
some “parameters” θ
Procedure (using θ)
that outputs a prediction
Score performance
(“cost function”)
Change θ
Improve performance
Linear regression
“Predictor”:
Evaluate line:
Target y
40
return r
20
0
0
10
Feature x
20
• Define form of function f(x) explicitly
• Find a good f(x) within that family
(c) Alexander Ihler
More dimensions?
26
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y
y
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22
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30
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40
20
x1
30
20
10
10
0
0
x2
(c) Alexander Ihler
40
20
x1
30
20
10
10
0
0
x2
Notation
Define “feature” x0 = 1 (constant)
Then
(c) Alexander Ihler
Measuring error
Error or “residual”
Observation
Prediction
0
0
20
(c) Alexander Ihler
Mean squared error
• How can we quantify the error?
• Could choose something else, of course…
– Computationally convenient (more later)
– Measures the variance of the residuals
– Corresponds to likelihood under Gaussian model of “noise”
(c) Alexander Ihler
MSE cost function
• Rewrite using matrix form
(Matlab)
>> e = y’ – th*X’;
J = e*e’/m;
(c) Alexander Ihler
J(θ)
Visualizing the cost function
θ1
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0
0
-10
-10
-20
-20
-30
-30
-40
-1
-0.5
0
0.5
1
1.5
2
2.5
(c) Alexander-40Ihler
-1
3
-0.5
0
0.5
1
1.5
2
2.5
3
Supervised learning
• Notation
–
–
–
–
Features
x
Targets
y
Predictions ŷ
Parameters q
Learning algorithm
Program (“Learner”)
Training data
(examples)
Features
Feedback /
Target values
Characterized by
some “parameters” θ
Procedure (using θ)
that outputs a prediction
Score performance
(“cost function”)
Change θ
Improve performance
Finding good parameters
• Want to find parameters which minimize our error…
• Think of a cost “surface”: error residual for that θ…
(c) Alexander Ihler
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Machine Learning and Data Mining
Linear regression: direct minimization
MSE Minimum
• Consider a simple problem
– One feature, two data points
– Two unknowns: µ0, µ1
– Two equations:
• Can solve this system directly:
• However, most of the time, m > n
– There may be no linear function that hits all the data exactly
– Instead, solve directly for minimum of MSE function
(c) Alexander Ihler
SSE Minimum
• Reordering, we have
• X (XT X)-1 is called the “pseudo-inverse”
• If XT is square and independent, this is the inverse
• If m > n: overdetermined; gives minimum MSE fit
(c) Alexander Ihler
Matlab SSE
• This is easy to solve in Matlab…
%
%
y = [y1 ; … ; ym]
X = [x1_0 … x1_m ; x2_0 … x2_m ; …]
% Solution 1: “manual”
th = y’ * X * inv(X’ * X);
% Solution 2: “mrdivide”
th = y’ / X’;
% th*X’ = y
(c) Alexander Ihler
=>
th = y/X’
Effects of MSE choice
• Sensitivity to outliers
18
16
16 2 cost for this one datum
14
12
Heavy penalty for large errors
10
5
8
4
3
6
2
4
1
0
-20
2
0
0
2
4
6
8
10
12
14
16
18
(c) Alexander Ihler
-15
20
-10
-5
0
5
L1 error
18
L2, original data
16
L1, original data
14
L1, outlier data
12
10
8
6
4
2
0
0
2
4
6
8
10
12
14
16
18
(c) Alexander Ihler
20
Cost functions for regression
(MSE)
(MAE)
Something else entirely…
(???)
“Arbitrary” functions can’t be
solved in closed form…
(c) Alexander Ihler
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Machine Learning and Data Mining
Linear regression: nonlinear features
Nonlinear functions
• What if our hypotheses are not lines?
– Ex: higher-order polynomials
Order 3 polynom ial
Order 1 polynom ial
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14
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12
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8
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4
4
2
0
2
0
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0
0
(c) Alexander Ihler
2
4
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Nonlinear functions
• Single feature x, predict target y:
Linear regression in new features
• Sometimes useful to think of “feature transform”
(c) Alexander Ihler
Higher-order polynomials
Order 1 polynom ial
18
• Fit in the same way
• More “features”
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14
12
10
8
6
4
2
0
Order 2 polynom ial
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16
14
0
2
4
6
8 3 polynom
10
12
Order
ial
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16
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20
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12
12
10
10
8
8
6
6
4
4
2
2
0
-2
0
2
4
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20
0
0
2
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Features
• In general, can use any features we think are useful
• Other information about the problem
– Sq. footage, location, age, …
• Polynomial functions
– Features [1, x, x2, x3, …]
• Other functions
– 1/x, sqrt(x), x1 * x2, …
• “Linear regression” = linear in the parameters
– Features we can make as complex as we want!
(c) Alexander Ihler
Higher-order polynomials
• Are more features better?
• “Nested” hypotheses
– 2nd order more general than 1st,
– 3rd order “ “ than 2nd, …
• Fits the observed data better
Overfitting and complexity
• More complex models will always fit the training data
better
• But they may “overfit” the training data, learning
complex relationships that are not really present
Complex model
Simple model
Y
Y
X
(c) Alexander Ihler
X
Test data
• After training the model
• Go out and get more data from the world
– New observations (x,y)
• How well does our model perform?
(c) Alexander Ihler
Training data
New, “test” data
Training versus test error
• Plot MSE as a function
of model complexity
30
– Polynomial order
Training data
25
• Decreases
20
15
10
• 0th to 1st order
– Error decreases
– Underfitting
• Higher order
– Error increases
– Overfitting
5
0
0
Mean squared error
– More complex function
fits training data better
New, “test” data
0.5
1
1.5
2
Polynomial order
(c) Alexander Ihler
2.5
3
3.5
4
4.5
5
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