Igor F.
  • Member for 1 year, 10 months
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How important is outcome variable scaling in SVM regression?
2 votes

This is a cross-posting from CrossValidated: In support vector regression (with linear loss), we minimise the objective function: \begin{align} \min_{\mathbf{w}, b, \mathbf{\xi}} \quad & \frac{...

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Which supervised machine learning algorithms assume normally distributed feature variables?
1 votes

If you are distinguishing between Statistics and Machine Learning, then you need to define boundary between the two, and that boundary is going to be opinion-based. It is a matter of definition which ...

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How to solve Ax = b for A
1 votes

As Kashra said, your "system" has an infinite number of valid solutions. However, there is one "canonical" solution, that might make more sense than others, depending what you are after. A matrix is ...

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Why bias is not considering in Regularization?
1 votes

The point of regularization is to avoid overfitting, and overfitting happens when you have too many predictor variables (i.e. neurons) contributing to the outcome. So, by regularization you are ...

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If ReLU is so close to being linear, why does it perform much better than a linear function?
1 votes

In addition to the ncasas' answer, which is good in my opinion, I'd like to point out that ReLU is computationally inexpensive, in contrast to sigmoid activation functions. They require only an if / ...

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K-Means anomaly detection not clustering anomalies
1 votes

Your example shows that K-means (and clustering in general) is not a suitable tool to detect anomalies. Anomalies are, by definition, points (observations) deviating from normality, however that ...

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Anomaly Detection/Novelty detection
1 votes

I believe you can use a classification algorithm where you manually overrepresent the "anomalies" class. By how much, depends on the cost induced by the anomalies. Just to illustrate what I mean: ...

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Self Organizing Map (SOM)
0 votes

Let's start with k-means: If you add class information on top of it, you get the learning vector quantization (LVQ). On the other hand, if you impose a topology on the means (force them on an elastic ...

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One Challenging Neural Network Question and MATLAB Simulation
0 votes

This question is similar to this one and this one, but seems to be ill-posed. Either because it implies an unknown (undefined) way how the neurons process the inputs, or because the provided solution (...

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How to measure correlation between several categorical features and a numerical label in Python?
0 votes

If you perform linear regression, encoding the categorical variables by dummy numerical variables, the p-value of the corresponding coefficients will show you whether they significantly affect the ...

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when x is a vector, derivative of vector diag(f'(x)) is formal notation?
Accepted answer
0 votes

You are partially right and partially wrong: $f'(\textbf{x})$ is a matrix, but $\text{diag}(f'(\textbf{x}))$ means taking the diagonal of that matrix and making a vector out of it.

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I intend to do classification modelling, but my target variable has only one value
0 votes

To expand on fuwiak's answer, you can cluster the current loan group, declare clusters to be classes, and see whether a good fraction from your default set gets classified in one of the classes/...

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Which machine learning algorithms can be used for trajectory classifications?
0 votes

Depending on the amount of data, any classification algorithm can be suitable. LSTM, however, are likely to be an overkill, considering that you probably won't be having much variation in the time ...

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What is the difference between classification and regression?
0 votes

Classification task: Predict (guess, estimate) a class (a nominal variable) based on some predictor variables, which can be of any type. Example: Based on the videos a user has watched over a video ...

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