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