Hi Shagun, The following documentation link might be helpful to interpret the result of PCA biplot: https://www.mathworks.com/help/stats/pca.html#btjpztu-1 You can refer to the section under the biplot command in the documentation page for more details: ThemeCopy biplot(coeff(:...
To interpret a machine learning model, we first need a model — so let’s create one based on theWine quality dataset. Here’s how to load it into Python: Wine dataset head (image by author) There’s no need for data cleaning — all data types are numeric, and there are no missi...
I understand from your introduction to Probability that the mode would give the expected value in such case. Can I interpret it so that the 5 Number Summary for a discrete variable will be based on occurences for each discrete value? So 50th quartile is the mode, Min is the value with ...
However, despite abundant literature on the topic, there is a lack of publications on how to actually interpret FCH-PET/CT in a clinical setting. Here we propose a practical, TNM-oriented approach to read FCH-PET/CT, with notes on procedure technique, image display, review sequence and ...
interpretability. Since we’re transforming the data, features lose their original meaning. This could be problematic in cases where interpretability of the data is important. However, in the feature selection example we mentioned earlier, there are cases where we can still partially interpret the ...
Principal Component Analysis is a tool that has two main purposes: To find variability in a data set. To reduce the dimensions of the data set. PCA examples
How do you decide on the number of PCs to use? If I go by the usual methods of choosing PCs I would do an elbow plot and take a point where the variance does not increase much more. Would that be a reasonable way to choose the number of PCs?
Factor analysis relies on several assumptions for accurate results. Violating these assumptions may lead to factors that are hard to interpret or misleading. Linear relationships between variables This ensures that changes in the values of your variables are consistent. ...
We can specify the axis as the dimension across which the operation is to be performed, and this dimension does not match our intuition based on how we interpret the “shape” of the array and how we index data in the array. As such, this causes maximum confusion for beginners. That is...
The ability to interpret and explain model outcomes is crucial in various contexts. However, the issue is that numerous algorithms function like “black boxes,” making explaining their results challenging, irrespective of how excellent they may be. The inability to do so can become a significant ...