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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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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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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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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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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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