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Significance test for comparing different 10-fold cross-validated Machine Learning Regressions

Here's my take on the situation:   won't running repeated cross-validations (and thus leading to an unlimited N mean that every comparison of models is infinitely good if you are as long as you run ...
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0 votes

When should I balance my data using AUROC and AUPRC?

ROC curves are insensitive to class imbalance. This means that if you re-balance your data, you will obtain (statistically) the same ROC curve. PR curves are sensitive to class imbalance, specifically ...
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Given a specific value for a variable, how do you find the predicted value of a fixed effects multivariate regression?

You have to use I(N^2) instead of N^2, because otherwise lm() interprets ...
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When there are many more controls than cases, can I take only part of the controls?

Can I take a random sample (e.g., 10,000) of 60,000 observations with disease (-) to build the predictive model? Let's try it out. The code below simulates data and fits two times the data one time ...
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How to predict multiple future values in a linear model in R?

To predict $credit_{t+1}$, you need $year_{t+1}$ (which you have) and $student_{t+1}$ (which you don't have). So first you need to create a model (or a formula) to predict that. Looking are your data, ...
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3 votes

Applications of Dynamic Time Warping (Time Series)

DTW is an algorithm for measuring the distance between two time series. It's an alternative to the Euclidean distance (which is the mean squared distance between the time series at each time step), ...
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  • 476
1 vote

How to label target dataset based on reduced dimensions of a source dataset?

You can apply the same principal component transformation from the source dataset to the target dataset. This will map your target dataset from the initial N dimensions to the same M dimensions, ...
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0 votes

Intuition for confidence intervals vs prediction intervals for linear regression

You can understand the confidence interval as an interval for the mean, which gives information about the uncertainty/variance of the model itself. A prediction interval is an interval for a single ...
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1 vote

Intuition for confidence intervals vs prediction intervals for linear regression

A confidence interval is for the mean of a group of people who have the same input values for your X. If all assumptions are met, 95% of the confidence intervals you calculate will contain the true ...
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1 vote

Negative prediction values from linear regression in R

[UPDATED] A typical times series interpretation doesn't apply in this case. It's more like a panel data, just to point out. There are more than one value per year. I suspect there are many models of ...
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15 votes
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Negative prediction values from linear regression in R

You have a linear fit that does not predict well for cars older than ten years. This is because most data points are for cars younger than 10 years old and these will dominate the fitting. If you ...
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6 votes

Negative prediction values from linear regression in R

You didn't constrain the output. Without such a constraint, you allow for any real number to be predicted, including numbers that are ridiculous. For instance, logistic regressions constrain the ...
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1 vote

Forcing covariates to always be part of a Lasso model

Lasso by default adds a regularization penalty for all the parameters, but nothing prohibits you from penalizing only some of the parameters. Running lasso and "adding back" the zeroed-out ...
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  • 113k
1 vote

Forecasting based on few samples

Your data can be plotted as follows: Note: Always plot your data! Especially if you want to forecast. In covid models, a V-shape recovery has been quite frequent. The blue line is your data. The red ...
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3 votes

Choice Between Alternatives in Machine Learning

It's not a machine learning problem and it is a bad idea. First, it is ethically dubious to have black-box software to make career decisions that would potentially influence the future of those ...
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  • 113k
0 votes

Prediction based on correlation

The parameters of the simple linear regression model $B = \alpha + \beta A + \varepsilon$ can be obtained from the standard deviations of both variables $s_A$ and $s_B$, the correlation coefficient $...
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2 votes

Features are Relevant for Regression but not necessarily for Classification - what to make of this?

Loosely speaking, I would interpret it to mean that a subset of features are most important for determining the direction (gain/loss), and then the other features come up in determining the magnitude. ...
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1 vote

How to plot the random and best model in Lift and gain charts?

I have never seen these kinds of plots before, but here's the idea. The "random" line is representing the results you would get if you did completely random guessing - so this is the "...
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6 votes
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Logistic Regression on multiple classes (Shouldn't it be only on binary?)

Extending my comment into an answer: There are several natural extensions to handle multiple classes, and two are built into scikit-learn. Multinomial logistic ...
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0 votes

Can I improve linear model coefficient estimates using group information without working it into model?

When estimating a mixed effects model we have to distinguish between marginal (sometimes called "unconditional") and conditional estimates. Conditioning refers to whether these estimates are ...
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1 vote
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When to drop correlated features?

"Significant correlation" would usually mean that you tested a null hypothesis that $\rho=0$. Depending on your sample size, such correlation may still be quite close to zero. Why would you ...
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0 votes

Accuracy failure example

You can start with the Why is accuracy not the best measure for assessing classification models? thread. it gets the average correctness of the predictions and in some cases its result can be ...
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  • 113k
0 votes

Logistic regression predictions dont work

The inverse logit function produces continuous values strictly between 0 and 1, while a confusion matrix is based on predictions in $\{0, 1\}$. So either you dichotomize the continuous predictions (...
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1 vote

Ridge classification: Interpreting prediction

Are use sure that when changing the labels you didn't reverse them? Because both results you got are the same, just the second one got the labels in reverse, $1 - 0.65 = 0.35$. Both encodings are ...
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  • 113k
0 votes
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Neural Nework and mathematical programming

We can use the ReLU activation function and derive a linear equation from a neural network. Further information is presented in this paper (doi: 10.1007/s10601-018-9285-6)
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  • 129
1 vote

Is underdispersion problemetic for predictive poisson models?

If you are only interested in the predictions, then a Poisson regression model and a quasiPoisson regression model gives identical predictions, whether there are under- or over-dispersion. The ...
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