Match Maker EE
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The problem of missing data has in data analysis obtained considerable attention. In their reference book [1] Rubin and Little define three mechanisms behind data becoming missing (definitions from ...

The subject of my Ph.D dissertation was to reveal the black-box properties of neural networks, specifically feed-forward neural networks, with one or two hidden layers. I will take up the challenge ...

The problem with AIC is that it does not take into account the stochastics of the parameter vector ${\boldsymbol { \beta}}$. Recall that in multiple regression, each estimate of the regression ...

You are asking about conditional independence: $S_1 \; {\rm INDEP} \; S_2 \mid C19$. The way you write the joint probability, as a product over the probabilities of each feature - that model assumes ...

I have practical experience with training classifiers from imbalanced training sets. There are problems with this. Basically, the variances of the parameters associated with the less frequent classes -...

Interesting question. First of all, classic linear regression was developed for applications where the scatter is normally distributed. If you plot the residual distribution it should have the classic ...

No such formula exists for the optimal sample size of machine learning models. The general answer is - it depends. There is the Vapnik–Chervonenkis dimension, which gives a very general expression of ...

The probability distribution is the actual mathematical function $P({\bf x}; \theta)$ that can assign a probability to each possible vector ${\bf x}$. It is given by the parameter vector $\theta$. The ...

Experience with many different datasets containing discrete or continuous input variables have shown me that normalization makes training much easier. The randomly selected set of initial weights is ...

Interesting questions posed. I will address the two questions for the use case of statistical classifiers in order to demarcate the analysis to a model domain we can oversee. Before embarking onto an ...

Training NNs is a practical task where you are only interested in well-performing neural networks. A learning session that ends up in a poor local minimum is quickly discarded as the solution is ...

For clarity, I make a distinction between classification models and regression models. A classification problem is when a statistical model (the classifier) is being trained to predict the category ...

The importance of a feature-variable depends on the distributiuon of all the other feature-variables used, for the classification problem at hand. Generally, when changing a feature-value $x_i$ can ...

Multiclass problems can be partitioned into a set of 1-versus-many classification problems. Statistical classifiers handle such differently - let me make a distinction Probabilistic classifiers ...

First, the term 'Naive Bayes' refers to the made assumption of conditional independence among feature variables, given the class outcome (that is, 'stroke' or 'no-stroke'). Taking the variables gender ...

In your case, I would turn to the nonparametric Komolgorov-Smirnov one-sample test. With this test you compare a set of measurements against a known cumulative distribution function. The expression to ...

Relevant question. The most simple autoencoder has the NN-topology: $n-k-n$ with $n$ the number of input/output nodes and $k$ the number of hidden nodes. We talk of encoding/decoding when $k<n$. ...

Your measurement process is somewhat complex. The following is the case $\begin{split} a &= {\cal A} + \epsilon \\ b &= {\cal B} + \epsilon \\ \epsilon &\sim {\rm u}(-e,e) \\ \\ d &= (... View answer Accepted answer 2 votes You can try performing a nonlinear regression prediction using a neural network. Common practice is to let the output node (the$y$your predict) not use a nonlinear activation function. You can use ... View answer Accepted answer 2 votes Sporty question. Why not use a binomial test for this purpose? You want to test whether your$H_1: P > 0.70$as opposed to the opposite$H_0: P \leq 0.70$The binomial test reasons on a fraction in ... View answer 2 votes I like the question. One point before the explanation. In statistics, we use a capital letter$P$for probability, as your prior. For probability densities a small letter$p$is used. The probability$...

Appropriate question. The added value of preprocessing depends on the type of classifier you will train. If you use nonparametric classifiers like C4.5 (ID3), CART, the multinomial classifier, the ...

Having done scientific research in Machine learning / Statistical pattern recognition for 17 years - I can come up with a few skills that make a wanted-for data scientist stand out from others. ...

The mutual information measure $I(X;Y)$ is nonparametric measure of probabilistic dependence between the variables $X$ and $Y$. As follows from wikipedia: "Intuitively, mutual information ...

Interesting deep question. The deeper answer lies in the underlying causality - the process being studied. Let me make a clear distinction. In classic physics we know that $F = m \cdot a \;$ and that ...

Interesting problem - which is most often overlooked in data science and machine learning. The output probabilities $\bf{y}$ are indeed estimates of the underlying (true) posterior probabilities (your ...

The short answer to your question is Bayesian modelling. Beta-distributed priors and Dirichlet priors - these are places to start with when you want to combine number statistics with export knowledge ...

A convolutional neural network with nonlinear activation functions performs nonlinear image processing. Let an $X \times Y$ 2D image be defined as $I(x,y)$ and a convolutional neural network as $NN(x,... View answer Accepted answer 2 votes Neural networks can learn to solve$c$-class classification problems, where$c\$ is the number of classes (categories) to be discriminated. The general goal is to categorize a set of patterns or ...