Your English writing platform
Discover LudwigSuggestions(5)
"feature importance" is a correct and usable phrase in written English.
It is commonly used in the context of data analysis or machine learning, where features refer to specific variables or characteristics of a dataset. Example: The results of the analysis showed that age and income were the two most important features in predicting customer satisfaction.
Exact(60)
We then selected the top 100, 200 and 300 features using the feature importance score to identify the most important features.
Furthermore, the important analysis of feature elements was carried out by using the conditional feature importance strategy.
Fig. 8 Relative feature importance in Davis.
Figure 3 Feature importance in tie persistence.
Herein, the efficient built-in feature importance selector of the DT algorithm was used.
Incrementally adding such features gives us a chance to look at the individual feature importance.
Investigating feature importance of each type of protein descriptor can provide insights into FP oligomerization.
Further, region-wise assessment may also be useful in adaptive feature importance weighting schemes.
In this paper, we discuss the problem of domain learning, which is related to feature importance.
In the DT algorithm, the estimation of feature importance is calculated from the feature usage based on information gain.
Figure 4 Word cloud visualization of feature importance according to the NB model (A) and RF model (B).
Write better and faster with AI suggestions while staying true to your unique style.
Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com