-
The section 2.1 of Lecture chapter, shows the statistical model of an OLS regression. When I read the interpretation of result, the coefficient does not make sense to me. I am not sure if I am reading…
-
datalib currently calculates what is supposed to be Pearson's mode skewness, but does so as:
`(avg - med) / std`
This is [non-parametric skewness](https://en.wikipedia.org/wiki/Nonparametric_skew)…
-
Some questions and suggestions came to mind when I read about the gradient descent method:
- In section [Gradient Descent](https://ml-course.github.io/master/notebooks/02%20-%20Linear%20Models.html…
-
# Goal
As a developer, I want to add features to the existing machine learning model (XGBoost), so that I can develop a more accurate machine learning model.
# Consider
1. Consider using Cryptocurr…
-
The package is producing strange results when features aren't standardized.
```r
library(glmnet)
library(sgdnet)
set.seed(1)
x
-
The ranger random forest model does provide an OOB estimate of error (the kind you would get by testing your model on a 'test' data subset), but I've been struggling to put to rest the question of mod…
-
# Expected Behavior
Add data types for Javascript (and derivatives) with 56 bit floats
# Actual Behavior
No relevant category to match against.
# Specifications
-
In "Linear Regression" the **"Stand. Estimate"** shows other values (bug?) than SPSS, JASP or R (software), when a categorical independent variable is being used. Shouldn't "Stand. Estimate" be identi…
-
[The Actual Difference Between Statistics and Machine Learning](https://towardsdatascience.com/the-actual-difference-between-statistics-and-machine-learning-64b49f07ea3)
-
There's a whole large body of work on dimensionality reduction which handles non linearity better - i.e. UMAP. https://umap-learn.readthedocs.io/en/latest/
Is it simple to just "drop" this in place…