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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 the explanation: A priori (Interpretable) vs. A posterio (Explainable) I found this quote to be very helpful and inline with my own thoughts (emphasis mine): ...


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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 $f(x)=1,\ E=1, \ \phi_1=1, \ \phi_2=-1$. Then your scaling factor is undefined, trying to divide by zero. Well OK, but you won't often get such exact numbers. Let's tweak them to $$f(x)=...


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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, it misses the true positive instances for which the indications are not so clear. But when looking at macro-recall it's more complex: it depends primarily on how ...


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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 bigger, it's less intuitive what your loss function should be, but the interpretation of your output would likely come from how you define this loss.


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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 have a (estimated) function: $$y = 0 + 1x .$$ This tells you that when $x$ goes up (down) by one unit, $y$ goes up (down) by one unit. In this case it is just a ...


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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 in one variable explain variation in the other variable? The coefficients from the regression table, on the other hand, describe the relation between y and ...


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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 reasonable approach by you. It is like the famous interview question "How many golf balls can you fit into a swimming pool?". Such a question is asked to ...


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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. Be sure to label the sounds properly for higher efficiency.


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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 most of your data points were distributed to left side and vise versa for negative skewness. Always we needs to ensure that data points on the graph needs to be equally distributed to form ...


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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 are basically shifting it and then scaling it down by a constant but $y$ is unchanged. So, if we imaging a line in a 2-D space, we are keeping the $y$ same and ...


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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) to sample the whole population (i.e. all UCLA students from China and Hong Kong) and measure their cultural adjustment. Therefore, we can take a sample from ...


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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 from two variables $X$ and $Y$: I like to read this loosely in terms of variance (or spread across each variable). It is asking, how do $x$ and $y$ scale ...


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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 values(weights) during back propagation and evaluate what pixels are contributing most. Many opensource implementation are available and on similar track, ...


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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 machine learning is a tool used to solve problems present in the domain of explainable machine learning. To define your answer: One shall use an interpretable ...


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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 explainability- feature importance for all training data local explainability- explanation of every prediction. all types of explainability on iris dataset, will be ...


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The interpretation of the output depends not only on the architecture of the network, but also on the final-layer activation functions and the training procedure. Most importantly, training a neural net requires you to choose a loss function, which describes how far off the predictions in the final layer are from ground truth. If you can specify a sensible ...


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