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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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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 ...
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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 ...
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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 ...

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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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 ...
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1 vote
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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. ...
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1 vote

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 ...
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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 ...
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1 vote

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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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1 vote
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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. ...
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1 vote

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) ...
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1 vote
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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 ...
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1 vote

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 vote

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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