# Questions tagged [machine-learning]

Machine learning algorithms build a model of the training data. The term "machine learning" is vaguely defined; it includes what is also called statistical learning, reinforcement learning, unsupervised learning, etc. ALWAYS ADD A MORE SPECIFIC TAG.

12,578 questions
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### Scikit-Learn SVC Porbability Function

I use scikit-learn to train a SVC with 'poly'-Kernel and propability-paramter enabled. Most of the time the prediction and the probability assigned to the prediction is correct. That means: ...
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### Pairwise matching of case and control group for a machine learning classifier

If I want to test whether an illness is associated with alterations of a dependent variable Y (example: grey matter volume) I can perform under some assumptions a t-test. If I am aware of a confounder ...
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### Topological rather than metric based machine learning theory?

The first notion of continuity in a math class is usually the one based on metric spaces. In particular, the $\epsilon,\delta$ definition of continuity. But in topology, a more general notion of ...
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### Time Series Analysis for a Newbie

I am a beginner in time series analysis and machine learning. I have a dataset where I want to analyse and predict a time series data. I have a pollutant variable and four meteorological parameters ...
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### Selecting the best subject's data and features to optimize the analysis

I am not good at statistical analysis. So I am posting here my case and looking for your kind suggestions. My case: I have data from subjects, which each subject has two similar runs that were ...
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### Which is faster: a bank with five lines of ten or one line of fifty?

I'm working on a probability question with mean and variance. Let's say that I have two banks. They are identical in every way, except that bank A has five lines with ten people each and bank B ...
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### Recreating figure from Elements of Statistical Learning [closed]

I am trying to recreate FIGURE 3.6 from Elements of Statistical Learning. The only information about the figure is included in the caption. To recreate the forward stepwise line my process is as ...
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### Creating Target Variable for time series change point detection

I am working on a time series data for which I intend to impliment machine learning model for detecting change point in time series data. This data is recorded fom machinary and we have to predict ...
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### Why 'sequentialfs' of MATLAB stops before the optimum feature subset is selected?

I am using "sequentialfs" of MATLAB to select features from 271 features for 871 subjects over 2 classes. I used the backward sequential function. I noticed the features selected after using the ...
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### Implications of deploying a predictive model overfitting training data but consistent in validation folds (classification)

If a model is build on very dirty data, it is common to not be able to prevent an overfitted result even with rigorous regularization attempts. However, it is also common that some lift-producing ...
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### When to use which classification model?

This is something that continues to give me trouble. Assuming I am working to extract a classification from a dataset and assuming I have the computing resources to do the necessary calculations (in ...
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### Determine confidence in estimate from past estimates

I have a population on the order of 10,000 samples. These samples represent individual estimates of a variable, and for each estimate I also have the actual value of the variable. For each sample I ...
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### About the time differences in the Bellman equation

I am trying to grasp fundamental mathematics behind the Reinforcement Learning and so far I have unterstood how the Value Iteration and Policy algorithms do converge (contractions, etc.) I have still ...
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### What is meant by 'Black box variational inference'?

I'm aware of the topic of variational inference (VI) however I'm not really sure what Black box VI is? In particular I am watching a video by David Blei titled Black box variational inference and on ...
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### are there any supervised learning methods that can only be applied to data with continuous features?

Are there any methods that exclusively work on continuous features? At first i imagined that linear models would demand this, but discrete values can be transformed and encoded such that they can be ...
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### How are the weights updated in the perceptron learning rule?

I'm considering a perceptron model. I know that when feeding observations from the training dataset to the model, if the model correctly classifies the input, then the weights for this input will not ...
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### Dependent and Independent Features

I have a data set that has 3 columns(Features), I would consider every feature to be independent. Col1, Col2, Col3 would all be independent, but Col4 would be the features strung together which would ...
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### Gradient Descent and Back Propagation in Neural Networks: derivation in vectorised form

I was learning deep learning from Andrew Ng's course on Coursera and in one of the programming assignments to code out Neural Networks from scratch, the formula for the derivative of cost function wrt ...
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### What are the best classification models with one feature?

I am looking for some insight on making a model with just one predictor. Let's assume the data are not linearly separable (because otherwise I assume it wouldn't matter which linear method to use). ...
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### For p>>n, where p is number of predictors and n is number of observations, is n P or P+N?

For p$\gg$n, where p is number of predictors and n is number of observations, is n P or P+N? For example, if I am building a binary classifier, and I have 165 positive and 165 negative observations, ...
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### Find true negatives in a confusion matrix

I'm trying to find the True negative in a confusion matrix, I have computed successfully from scratch the precision and recall/sensibility, now i need to compute the accuracy and specificity. This is ...
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### How are the various guarantees provided to SVMs by Statistical Learning Theory affected by the Kernel Function

I never studied the field in depth, but I am very aware that state of the art performance in most ML tasks is now achieved by various flavors of neural networks. At the same time, Vladimir Vapnik, ...
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### Quantify the over/underrepresentation of data satisfying some condition(s)

I am doing binary classification on a dataset. It has the following distribution across a certain feature that takes on 13 possible values: Then I have this distribution which is a subset of the data ...
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### What is wrong with tuning parameters on training set as opposed to validation set?

When creating a machine learning model it is suggested to split your data into train, validation, and test sets. Here is my understanding of what they are for. Train: Use this to train the different ...