Yes, it is generally recommended to standardize data before applying PCA, especially when the variables in your dataset are measured on different scales or have different units of measurement.
Standardization transforms the data to have a mean of zero and a standard deviation of one. This process ensures that each variable contributes equally to the analysis and prevents variables with larger scales from dominating the variance explained by the principal components.
Skipping the step of standardization when applying PCA might be appropriate or necessary in certain contexts. In the case of the fisheriris dataset, it is because the data has homogeneous units and scales. If all variables in your dataset are measured in the same units and have approximately the same scale and variance, standardization might not be necessary. In such cases, the original scales of the variables are meaningful, and preserving these scales might be important for interpretation. For example, if all variables are measurements of length in centimeters, standardizing would remove this common scale, potentially making the results less interpretable.
