Exam AWS Certified Machine Learning - Specialty topic 1 question 44 discussion

Yes, answer is BCF

Go for BCF

I think here the point is around the definition of "feature combinations". If you refer to it as "combine the features to generate a smaller but more effective feature set" this would end up to a smaller feature set thus a good thing for overfitting. However, if you refer to it as "combine the features to generate additional features" this would end up to a larger feature set thus a bad thing for overfitting. Also, in some cases you implement feature combinations in your model (see hidden layers in feed-forward network) thus increasing model complexity which is bad for overfitting. To me this question is poorly worded. I would pick F as my best guess is that you need to implement feature combination in your model, thus decreasing feature combination decrease complexity hence improving with overfitting issue

About option E: When increasing feature combinations, the goal is not to simply add more features indiscriminately, which could indeed lead to overfitting. Instead, it involves selecting and combining features in a way that captures important patterns and relationships in the data. When done effectively, increasing feature combinations can help the model generalize better to unseen data by providing more informative and discriminative features, thus reducing the risk of overfitting.

If your model is overfitting the training data, it makes sense to take actions that reduce model flexibility. To reduce model flexibility, try the following: Feature selection: consider using fewer feature combinations, decrease n-grams size, and decrease the number of numeric attribute bins. Increase the amount of regularization used. https://docs.aws.amazon.com/machine-learning/latest/dg/model-fit-underfitting-vs-overfitting.html

Best choices are B (Increase regularization), C (Increase dropout), and F (Decrease feature combinations), as these techniques are effective in reducing overfitting and improving the model's ability to generalize to new data.

BCE The model has learnt training data. One approach is to increase complexity by increasing the features or remove some features to increase bias. In deep learning, i thinking increasing feature set is more workable.

B-C-F. All of those options can be used to reduce model complexity and thus: overfit

its BCF

BCF is correct.

Increasing regularization helps to prevent overfitting by adding a penalty term to the loss function to discourage the model from learning the noise in the data. Increasing dropout helps to prevent overfitting by randomly dropping out some neurons during training, which forces the model to learn more robust representations that do not depend on the presence of any single neuron. Decreasing the number of feature combinations helps to simplify the model, making it less likely to overfit.

I see all the comments for BCF, although when you look at F it just says decrease 'feature combinations', not features themselves. In one way to decrease feature combinations results in having more features (less feature engineering), which in turn will cause more overfitting. Unless the question in badly worded, saying less feature combinations just mean those combinations, which components will not be used, then it has to be BCE.

BCF - Always remember in case of overfitting - reduce features, Add regularisation and increase dropouts.

BCE: The main objective of PCA (technic to feature combination) is to simplify your model features into fewer components to help visualize patterns in your data and to help your model run faster. Using PCA also reduces the chance of overfitting your model by eliminating features with high correlation. https://towardsdatascience.com/dealing-with-highly-dimensional-data-using-principal-component-analysis-pca-fea1ca817fe6

B/C/F Easy peasy.

BCF 100%

BCF F explained in AWS document: Feature selection: consider using fewer feature combinations, decrease n-grams size, and decrease the number of numeric attribute bins. Increase the amount of regularization used https://docs.aws.amazon.com/machine-learning/latest/dg/model-fit-underfitting-vs-overfitting.html