Questions tagged [non-parametric]
The non-parametric tag has no usage guidance.
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Is SVM a good choice for this imputing a numerical variable?
Let's say I have 10,000 training points, 100,000,000 points to impute, and 5-10 prediction variables/parameters, all numeric (for now). The target variable is numeric, skewed normal with outliers. I ...
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How do you use KS-test in a data science report?
I'm writing a data science report, I want to find an exist distribution to fit the sample. I got a good looking result , but when I use KS-test to test the model, I got a low p-value,1.2e-4, ...
CommunityBot
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Nonparametric Outlier Detection
Which Nonparametric outlier detection do you suggest to detect outliers (red points) in these plots? I have tested std, IQR, etc., but no good result. It is just one vector including normal and ...
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How to automatically segment multidimensional data?
How to partition the time-series multidimensional data in the figure below into segments using an unsupervised algorithm, so that the information within the same segment remains consistent, while the ...
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Multiple regression with non-normal data in minitab - help
I am aiming to assess the effect of BMI (continuous) on certain biomarkers (also continuous) whilst adjusting for several relevant variables (mixed categorical and continuous) using multiple ...
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Do non-parametric models always overfit without regularization?
Let's scope this to just classification.
It's clear that if you fully grow out a decision tree with no regularization (e.g. max depth, pruning), it will overfit the training data and get full accuracy ...
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pass variable length argument to mstats.kruskalwallis
I am trying to run kruskawallis test on multiple columns of my data for that i wrote an function
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CommunityBot
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what are the main differences between parametric and non-parametric machine learning algorithms?
I am interested in parametric and non-parametric machine learning algorithms, their advantages and disadvantages and also their main differences regarding computational complexities. In particular I ...
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Books about statistical inference [closed]
I'm currently taking a course "Introduction to Machine Learning" which covers the following topics:
linear regression, overfitting, classification problems, parametric & non-parametric ...
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Should features be correlated or uncorrelated for classification?
I have seen researchers using pearson's correlation coefficient to find out the relevant features -- to keep the features that have a high correlation value with the target. The implication is that ...
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Logic behind the Statement on Non-Parametric models
I am currently reading 'Mastering Machine Learning with scikit-learn', 2E, by Packt. In Lazy Learning and Non-Parametric models topic in Chapter 3- Classification and Regression with k-Nearest ...
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Good introductory reference for Bayesian Non-parametric (Dirichlet Process / Chinese Restaurant Process)
I am looking for a recommendation for basic introductory material on Bayesian Non-parametric methods, specifically Dirichlet Process / Chinese Restaurant Process. I am looking for material which ...
CommunityBot
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Linear vs Non linear regression (Basic Beginner)
So my doubt is basically in Linear regression, We try to fit a straight line or a curve for a given training set. Now, I believe whenever the features (independent variable) increases, parameters also ...
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About confidence/prediction intervals: parametric methods VS non-parametric (via bootstrap) methods
About the methodology to find confidence and/or prediction intervals in, let's say, a regression problem, I know 2 main options:
Checking normality in the estimates/predictions distribution, and ...
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Non-parametric regression on set of time series: One model for each or one for all series?
Let's say I have a set of 1D time series which values have been samples in equip-distant time steps with timestamps $1,2,3,...$, they have all the same lengths and are somewhat similar in shape. I ...
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