maggicl/ml
Archived
1
0
Fork 0
Code Issues Pull requests Projects Releases Wiki Activity

wip

Browse source
This commit is contained in:
Claudio Maggioni (maggicl) 2021-05-04 16:18:52 +02:00
parent 7fb4401bbc
commit 68712ae0e0
1 changed files with 9 additions and 4 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

13
assignment_1/report_Maggioni_Claudio.md
View file

@ -112,7 +112,7 @@ better.
the training error curve decreases as the model complexity increases, albeit
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.**
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
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?**
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
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?**
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
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?**
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
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
activation function $h(x) = - x \cdot e^{-2}$ for the hidden units?**