15
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What is the difference between explainable and interpretable machine learning?
I found this article by Cynthia Rudin which goes a bit more into the difference between the two terms that is in line with your source from O'Rourke.
At the core it is about the time and mechanism of ...
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4
votes
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Interpreting ROC curves across k-fold cross-validation
$k$-fold cross-validation simply repeats the same process with different parts of the data. Therefore any difference between different folds can only be due to chance, i.e. it's only because different ...
- 25.3k
3
votes
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Shapley values without intercept (or without `expected_value`)
This is very similar to fitting a linear regression and not including an intercept, and I think they will face similar issues.
To be very concrete, consider an example with $f(x)=1,\ E=1, \ \phi_1=1, \...
- 11.8k
3
votes
Is there an intuitive interpretation of precision always higher than recall?
Generally lower recall means that the system is too strict, i.e. it predicts an instance as positive only when it has clear indications in the features that it's indeed a positive. As a consequence, ...
- 25.3k
3
votes
How do standardization and normalization impact the coefficients of linear models?
When you have a linear regression (without any scaling, just plain numbers) and you have a model with one explanatory variable $x$ and coefficients $\beta_0=0$ and $\beta_1=1$, then you essentially ...
- 7,456
3
votes
A neural network with more output neurons than labels
The output layer is usually the same size as the last dense layer because we apply a loss function to train the model by comparing the last layer to what the output should be. If your output layer was ...
- 400
2
votes
What is the difference between explainable and interpretable machine learning?
As for as explanation is concerned, we need explainability/interpretability at every level-
data explanation- tsne, simple plotting.
model explainability- by creating surrogate models
global ...
- 425
2
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How to interpret Correlation along with Coefficients of multiple linear regression ?£
The table of correlation coefficients shows the pairwise correlation between the variables in your data set: on a range from 0 (no correlation) to 1 (full correlation), to what extent does variation ...
- 21
2
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Does Karl Pearson correlation indicate linear relationship between two variables?
The Pearson correlation coefficient does indeed quantify the linear relationship between two variables.
Have a look at one of the many mathematical formulas to compute it, based on a sample of data ...
- 14.9k
2
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What is the difference between explainable and interpretable machine learning?
Explainable Machine Learning is the domain of AI. It consists of interpretable models. One could say the difference is that one is a tool and the other is a field of study.
In brief, interpretable ...
- 482
2
votes
How to interpret KDE distribution graph?
Please find beautiful, explanation about KDE, In your graph on X Coordinateif the tail is stretching long towards right side then its positively skewed, it means ...
- 31
2
votes
Accepted
Which intrinsically explainable model has the highest performance?
To add a bit more to @noe 's answer: when you have a small number of features, explainable models can do a lot for you because they usually operate by making a prediction directly using the input ...
- 158
2
votes
Accepted
How to interpret a linear regression effects graph?
Note: you didn't mention what this is for, i.e. the target variable that this model is supposed to predict.
Anyway this graph shows for each independent variable (feature) its effect on predicting the ...
- 25.3k
2
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How do I combine two different measures of correlation coefficients?
It really depends on what you want to demonstrate. In case you want to stricly give an indication about the strength of the (potentially non-existent) linear relationship, then use the Pearson ...
- 358
1
vote
Accepted
SHAP values are explaining the wrong output value
In the latest lgbm version, predict does not output raw scores, instead it outputs probabilities for binary classification task. The above code will work without using logits if model_output is ...
- 11
1
vote
Accepted
ValueError: X has 54 features, but DecisionTreeClassifier is expecting 53 features as input
Where you do pred_set.drop(["Winner"], axis=1) you need to add inPlace=True, or assign to a new dataframe, otherwise nothing happens.
...
- 1,330
1
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Interpreting model
Yes - that is probably overfitting.
There is a chance that your distribution of test set, is different to your training or validation sets - but this is quite rare.
Add some form of regularisation to ...
- 435
1
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Interpreting visualisations for write ups - clustermaps
If you have a lot of features isn't likely to have a easy interpretation out-of-the-box despite the reder and bluer ones have more positive and negative correlation and the white ones don't have as ...
- 131
1
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Which intrinsically explainable model has the highest performance?
I think there are two issues with the formulation of your question:
Model performance highly depends on the data.
Explainability is not a black/white concept.
That said, one may understand that ...
- 26.6k
1
vote
Efficient ways of clustering for big data
1- Establish general rules and patterns with a random sample of ~2% of total rows. Even if the data is significant, you have always to get "the big picture" using a random and representative ...
- 4,684
1
vote
How do I combine two different measures of correlation coefficients?
Pearson vs Spearman vs Kendall
Averaging the results from the Pearson and Spearman coefficients does not make sense in this case. The Pearson coefficient measures the linear relationship between two ...
- 3,848
1
vote
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Answering the question of "WHY" using AI?
In short: no, one cannot feed a ML system with massive random heterogeneous data and expect the system to make sense of it by itself.
ML is not magical, it needs to be fed with the right information ...
- 25.3k
1
vote
What are available Python libraries for Interpretable ML?
A couple of the most common Python packages for interpretable machine learning:
Lime - Can explain the prediction of any machine learning classifier.
SHAP - A game-theoretic approach to explain the ...
- 21.2k
1
vote
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How to increase sales and revenue of a Client?
I think the question was asked to see how would you approach the problem. In similar questions, there is not a single answer, and the interviewer does not expect a certain answer instead expects a ...
- 1,148
1
vote
Accepted
Machine learning, speech recognition technologies for Sound of Animals interpretation
Yes it is very easily possible.
If you want quick output use teachable machine.
https://github.com/seth814/Audio-Classification Here is a sample git rep which you could use to enter into the domain. ...
- 482
1
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How do standardization and normalization impact the coefficients of linear models?
I believe with scaling, the coeff. are scaled by the same level i.e. Std. Deviation times with Standardization and (Max-Min) times with Normalization
If we look at all the features individually, we ...
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How do I interpret the output of linear regression model in R?
So, the question is centred around the meaning behind a confidence interval.
The main principle behind confidence intervals is the following:
It is very costly and time-inefficient (if not impossible) ...
- 1,438
1
vote
Accepted
Feature-to-parameter mapping in neural networks
Yes, at least you can identify what pixels' are contributing most in the prediction.
Tool like Layerwise Relevance Propagation, used for Explainable AI, serves the similar purpose and evaluate the ...
- 1,262
1
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What is the difference between explainable and interpretable machine learning?
Interpretability can be seen as a passive chracteristic of the model that referees to which level a given model makes sense for a human observer.
Explainability can be viewed as an active ...
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1
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What is the difference between explainable and interpretable machine learning?
In my opinion, the interpretability of an ML model refers to the ability to understand how the ML model is formed.
Normally, an ML model is created by using some intuitions. However, if the model is ...
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