Data Science Stack Exchange Community Digest

Top new questions this week:

Multicollinearity vs Perfect multicollinearity for Linear regression

I have been trying to understand how multicollinearity within the independent variables would affect the Linear regression model. Wikipedia page suggests that only when there is a "perfect" ...

linear-regression collinearity

asked by aKummari 5 votes

answered by Marjolein Fokkema 7 votes

How to create a confusion matrix for k-means with two features?

I have the need to do a confusion matrix for data run through k-means with two features. I am aware that this is a clustering algorithm and not a classification algorithm but I have seen some ...

k-means confusion-matrix

asked by Colin Crook 4 votes

answered by Erwan 4 votes

What changes is the Neural Network back-propagation algorithm doing on the weights?

I have seen the formula for back-propagation algorithm for neural network error minimization, but I am not quite sure about what changes it is performing on the weights individually. Let us suppose a ...

machine-learning neural-network training algorithms backpropagation

asked by James 3 votes

answered by Oscar 0 votes

How to combine binary classification with patient stratification?

I am working on a binary classification model (healthy/diseased) based on gene expression data of different patients. As a second task, I would like to stratify these patients and find subgroups. I ...

clustering binary-classification

asked by vhio 3 votes

answered by Erwan 2 votes

classification balanced target y [0,1] but imbalanced feature x [many 0 , few 1s] , maximize precision

I have a simple dataset with balanced target y (0 or 1) ,and imbalanced feature (many 0 , few 1's) I aim to get high precision (don't care about recall) I can get high precision of 0.53 if I just ...

keras decision-trees xgboost

asked by alexprice 3 votes

answered by Erwan 1 vote

Does PCA helps to include all the variables even if there is high collinearity among variables?

I have a dataset that has high collinearity among variables. When I created the linear regression model, I could not include more than five variables ( I eliminated the feature whenever VIF>5). But ...

linear-regression pca collinearity

asked by Naseef 3 votes

answered by Peter 0 votes

What are the most known ML-models that use complex numbers? (if there are any)

Basically just the header. The question is out of curiosity as I haven't seen one yet.

machine-learning-model model-selection

asked by Moritz Groß 3 votes

answered by Nikos M. 3 votes

Greatest hits from previous weeks:

Backprop Through Max-Pooling Layers?

This is a small conceptual question that's been nagging me for a while: How can we back-propagate through a max-pooling layer in a neural network? I came across max-pooling layers while going through ...

neural-network backpropagation

asked by shinvu 95 votes

answered by abora 98 votes

Should a model be re-trained if new observations are available?

So, I have not been able to find any literature on this subject but it seems like something worth giving a thought: What are the best practices in model training and optimization if new observations ...

machine-learning predictive-modeling optimization training

asked by 119631 52 votes

answered by Hima Varsha 29 votes

How to set class weights for imbalanced classes in Keras?

I know that there is a possibility in Keras with the class_weights parameter dictionary at fitting, but I couldn't find any example. Would somebody so kind to ...

deep-learning classification keras weighted-data

asked by Hendrik 229 votes

answered by layser 192 votes

Why do cost functions use the square error?

I'm just getting started with some machine learning, and until now I have been dealing with linear regression over one variable. I have learnt that there is a hypothesis, which is: $h_\theta(x)=\...

machine-learning linear-regression loss-function

asked by Golo Roden 103 votes

answered by Harsh 69 votes

Advantages of AUC vs standard accuracy

I was starting to look into area under curve(AUC) and am a little confused about its usefulness. When first explained to me, AUC seemed to be a great measure of performance but in my research I've ...

machine-learning accuracy

asked by aidankmcl 92 votes

answered by indico 82 votes

In softmax classifier, why use exp function to do normalization?

Why use softmax as opposed to standard normalization? In the comment area of the top answer of this question, @Kilian Batzner raised 2 questions which also confuse me a lot. It seems no one gives an ...

machine-learning deep-learning

asked by Hans 49 votes

answered by vega 63 votes

What's the difference between fit and fit_transform in scikit-learn models?

I do not understand the difference between the fit and fit_transform methods in scikit-learn. Can anybody explain simply why we ...

python scikit-learn

asked by Kaggle 221 votes

answered by K3---rnc 221 votes

Can you answer these questions?

subtracting from multiple rows in a cvs file in python

I am trying to figure out how to subtract a set amount from a row if it goes over a value. For example, if I have a list of library accounts and I need to write a function whereby if an individual ...

python dataset code

asked by Julie 1 vote

What is the best model for predicting delays?

Supposing we need to predict delays based on a previous dataset that contains the history of several, lets say, providers and their delivery delays. The goal is to minimize the loss due to those ...

classification linear-regression forecasting

asked by Alex Javarotti 1 vote

Building a simulator for continuous state, discrete action reinforcement learning

I am trying to build a simulator that optimizes the performance and temperature of a device. I want the device to perform well, but without making the device too hot. If the device becomes too hot, I ...

reinforcement-learning simulation

asked by user126806 1 vote