Are decision trees affected by feature scaling?
Decision trees and ensemble methods do not require feature scaling to be performed as they are not sensitive to the the variance in the data.
What is sparse Modelling in machine learning?
Sparse modeling is a rapidly developing area at the intersection of statistical learning and signal processing, motivated by the age-old statistical problem of selecting a small number of predictive variables in high-dimensional datasets.
Is random forest sensitive to scaling?
No, scaling is not necessary for random forests. The nature of RF is such that convergence and numerical precision issues, which can sometimes trip up the algorithms used in logistic and linear regression, as well as neural networks, aren’t so important.
Does random forest requires scaling?
Random Forest is a tree-based model and hence does not require feature scaling. This algorithm requires partitioning, even if you apply Normalization then also> the result would be the same.
What happens when a model has sparse features?
If the model has many sparse features, it will increase the space and time complexity of models. Linear regression models will fit more coefficients, and tree-based models will have greater depth to account for all features. Model algorithms and diagnostic measures might behave in unknown ways if the features have sparse data.
What are the problems with sparse features in machine learning?
Common problems with sparse features include: If the model has many sparse features, it will increase the space and time complexity of models. Linear regression models will fit more coefficients, and tree-based models will have greater depth to account for all features.
Why are tree based ensembles so tarnished?
In spite of delivering better performance, the tree based models have a slightly tarnished reputation primarily because of its interpretability which arises due to the way in which the feature importance is calculated. Feature importance in most tree-ensembles is calculated based an importance score.
How are categorical variables disadvantaged in decision trees?
The resulting tree from a dataset containing a majority of continuous variables that would look like something in the figure to the left. Categorical variables are naturally disadvantaged in this case and have only a few options for splitting which results in very sparse decision trees.