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Claudio Maggioni (maggicl) 2021-05-04 16:18:52 +02:00
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assignment_1/report_Maggioni_Claudio.md
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@ -112,7 +112,7 @@ better.
the training error curve decreases as the model complexity increases, albeit the training error curve decreases as the model complexity increases, albeit
in a less steep fashion as its behaviour in (a). in a less steep fashion as its behaviour in (a).
1. **Is any of the three section associated with the concepts of 2. **Is any of the three section associated with the concepts of
overfitting and underfitting? If yes, explain it.** overfitting and underfitting? If yes, explain it.**
Section (a) is associated with underfitting and section (c) is associated Section (a) is associated with underfitting and section (c) is associated
@ -143,7 +143,7 @@ better.
data without learning noise. Thus, both the validation and the test MSE data without learning noise. Thus, both the validation and the test MSE
curves reach their lowest point in this region of the graph. curves reach their lowest point in this region of the graph.
1. **Is there any evidence of high approximation risk? Why? If yes, in 3. **Is there any evidence of high approximation risk? Why? If yes, in
which of the below subfigures?** which of the below subfigures?**
Depending on the scale and magnitude of the x axis, there could be Depending on the scale and magnitude of the x axis, there could be
@ -158,7 +158,7 @@ better.
test error, since the inherent structure behind the chosen family of models test error, since the inherent structure behind the chosen family of models
would be unable to capture the true behaviour of the data. would be unable to capture the true behaviour of the data.
1. **Do you think that by further increasing the model complexity you 4. **Do you think that by further increasing the model complexity you
will be able to bring the training error to zero?** will be able to bring the training error to zero?**
Yes, I think so. The model complexity could be increased up to the point Yes, I think so. The model complexity could be increased up to the point
@ -170,7 +170,7 @@ better.
learned as well, thus making the model completely useless for prediction of learned as well, thus making the model completely useless for prediction of
new datapoints. new datapoints.
1. **Do you think that by further increasing the model complexity you 5. **Do you think that by further increasing the model complexity you
will be able to bring the structural risk to zero?** will be able to bring the structural risk to zero?**
No, I don't think so. In order to achieve zero structural risk we would need No, I don't think so. In order to achieve zero structural risk we would need
@ -197,6 +197,11 @@ Comment and compare how the (a.) training error, (b.) test error and
justify the poor performance of your perceptron classifier to your justify the poor performance of your perceptron classifier to your
boss?** boss?**
The classification problem in the graph, according to the data points
shown, is quite similar to the XOR or ex-or problem. Since in 1969 that
problem was proved impossible to solve by a perceptron model by Minsky and
Papert, then the
1. **Would you expect to have better luck with a neural network with 1. **Would you expect to have better luck with a neural network with
activation function $h(x) = - x \cdot e^{-2}$ for the hidden units?** activation function $h(x) = - x \cdot e^{-2}$ for the hidden units?**