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G. Explanation Phase
Lectures
8
Difficulty
Medium
Homework
Yes
Small Description
Explaining the model.
journals
Gallery
Defining Explainability
We can more or less define an "explainable" model as one that can give you are
reasonable estimate
to the following questions:
•
Some problems are just
too complex
to explain, e.g, 20-layer neural network with 4,000 features.
•
It's exactly their intractability to our brains that makes them ideal for equation-generating algorithms to solve.
•
It is for that reason that some firms don’t care too much about explainability, anecdotally, firms like RenTech sometimes have no idea why their models are doing what they are doing.
Importance Measures
Interpretable Models
For
interpretable
models you know the
exact
contribution of every feature to the final output. For
uninterpretable
models you only have an
estimate
of each feature’s contribution to the final output.
•
Interpretable models (white-box) are inherently explainable, we don’t need to use methods like Permutation or Shapley value calculation to identify the feature effects.
•
Uninterpretable (black-box) models are not interpretable by nature, as such we need to use explainability methods like calculating
Permutation importance
or
Shapley values
.
•
Explainability methods seeks to close the gap in understanding between white-box and black-box models.
•
No matter how many explainability methods you use, they will always be estimates, and will never give you the intrinsic explanations of say linear regression models, i.e., parameter coefficients.
A. Interpretability and Explainability
Model Specific
Importance Measures
•
Importance measures are quantitative values that reflect the
importance of each feature
to the final
predicted outcome
.
•
Most models give you two of three values: direction, size, and significance.
•
Importance measures do not reflect the
intrinsic predictive value
of a feature and instead how important this feature is for a
particular model
.
In the previous section we have shown that importance values can be
model-specific
or
model agnostic
. In this topic, we will focus on global and local model-specific importance models, starting with
linear regression
models and
decision trees
.
Linear Explainability
•
If your model is more accurate, then not only are the predictions more accurate, but also the explainability outputs.
•
If a logistic regression has really good out-of-sample performance then the coefficients of the regression can be used to identify the most important features.
$\log \left(\frac{P_{i}}{1-P_{i}}\right)=\beta_{0}+\beta_{1} X_{1}+\beta_{2} X_{2} \ldots \beta_{k} X_{k}$
•
If the performance of the logistic regression model is good as measured by scores like Accuracy, F-1, or ROC (AUC) score, then we can trust the
$\beta_{k}$
values more.
•
The coefficient values should also only be trusted when the data has been effectively normalized.
•
Through standardizing (subtract mean, divide by standard deviation of each feature) you effectively change the units to standard deviations above/below the mean.
•
So, a one standard deviation increase in
$X_{1}$
corresponds to a
$\beta_{1}$
increase in the log odds.
B. Model-Specific Importance
Model Agnostic Methods
•
Previously we have looked at
model-based importance
methods like finding the coefficients of linear regression models.
•
Previously we have looked at
model-based importance
methods like finding the coefficients of linear regression models.
•
All model-based methods (regression parameters, split counting, and Mean Decrease Impurity) uses
in-sample performance
to estimate feature importance.
•
Model agnostic
methods can both be performed
in-sample
and
out of sample
, we will start by looking at
Mean Decrease Accuracy (MDA)
and
Single Feature Importance (SFI)
.
•
These methods can be applied to
any classifier
or
regression model.
•
Model Based:
we have see that there are some feature importance measure that only relates to specific models of interest, linear regression, decision trees, ensemble tree models etc.
•
Model Agnostic:
there are also a range of model that can be applied to any model, as such they become good methods for black-box and obscure models.
Global Methods
•
These methods can either be global and only apply at the feature level (column) or local and apply to every prediction sample (row).
•
As a result local methods can be turned into global methods by simply averaging over all the prediction samples in the dataset.
•
Whereas global methods can never be used to obtain local explanations due to their design.
C. Model-Agnostic Importance
Machine Learning in Finance (Blog Post)
•
Due Date: Apr 18, 2023 11:59 PM
•
Submit as PDF or Blog Post Link
Finding a Topic
Write a
1000-1500
word blog post about
any
topic in machine learning in finance, here are
four
ideas
1.
Summarize some
research paper
to make it easy to understand.
2.
Write a blog
about
your own code/project
, it could be an old or a new project.
3.
Write about a
coding competition
that you participated in.
4.
Think about
anything else
that you can write about that uses a prediction model in finance.
Some Points
1.
It doesn’t have to be your own idea, you can find the idea anywhere (including in research papers).
2.
Please don’t copy another person’s blog post, only use them for inspiration!
3.
It would be helpful to include some math formulas/and or some code snippets throughout the blog post.
4.
You do have to write code and can share snippets of your code throughout the blog, or simply post it in an appendix (or share your notebook/colab).
D. ML in Finance Post
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