Is machine learning merely a form of curve-fitting?
#machinelearning #ai #curvefitting #linearregression #buzzwords
Is machine learning merely a form of curve-fitting?
#machinelearning #ai #curvefitting #linearregression #buzzwords
Accuracy! To counter regression dilution, a method is to add a constraint on the statistical modeling.
Regression Redress restrains bias by segregating the residual values.
My article: http://data.yt/kit/regression-redress.html
How to assess a statistical model?
How to choose between variables?
Pearson's #correlation is irrelevant if you suspect that the relationship is not a straight line.
If monotonic relationship:
"#Spearman’s rho is particularly useful for small samples where weak correlations are expected, as it can detect subtle monotonic trends." It is "widespread across disciplines where the measurement precision is not guaranteed".
"#Kendall’s Tau-b is less affected [than Spearman’s rho] by outliers in the data, making it a robust option for datasets with extreme values."
Ref: https://statisticseasily.com/kendall-tau-b-vs-spearman/
Redressing #Bias: "Correlation Constraints for Regression Models":
Treder et al (2021) https://doi.org/10.3389/fpsyt.2021.615754
"In real life, we weigh the anticipated consequences of the decisions that we are about to make. That approach is much more rational than limiting the percentage of making the error of one kind in an artificial (null hypothesis) setting or using a measure of evidence for each model as the weight."
Longford (2005) http://www.stat.columbia.edu/~gelman/stuff_for_blog/longford.pdf
An easy guide to predict possible future quantities, by Mercy Kibet: https://www.influxdata.com/blog/guide-regression-analysis-time-series-data/#heading0
And the last question for ML people: Not so long ago, if I remember correctly, it wasn't possible to train a model to learn addition with real numbers outputting 100% accurate answers. I mean, it can be done even using linear regression, but the answers won't be 100% accurate for all cases.
Does this still hold true or am I wrong? Wouldn't it be overfitting?
One question for ML people: Machine learning was "taught" to me, back in the day as something that should only be used when 1) There is no exact #algorithm or tool to code what we want, and, 2) We don't need exact answers (ie, 100% accuracy).
Does this still hold true? My understanding is that it hasn't ever changed, or did it?