- What happens if load is unbalanced?
- Why is class imbalance a problem?
- What are the challenges with imbalanced class?
- Does XGBoost handle class imbalance?
- Does XGBoost need scaling?
- How do you reduce false positives in XGBoost?
- Can XGBoost handle categorical variables?
- Is LightGBM better than XGBoost?
- Is CatBoost faster than XGBoost?
- Which is better XGBoost or CatBoost?
What happens if load is unbalanced?
The loads will draw the same amount of power but due to unbalanced voltage this will effect current wave forms causing increased stress on all of the components. With motors you can hear it by an increase in noise and see it as torque and speed issues plus an extreme temperature rise.
Why is class imbalance a problem?
Why is this a problem? Most machine learning algorithms assume data equally distributed. So when we have a class imbalance, the machine learning classifier tends to be more biased towards the majority class, causing bad classification of the minority class.
What are the challenges with imbalanced class?
- Imbalanced classification is specifically hard because of the severely skewed class distribution and the unequal misclassification costs.
- The difficulty of imbalanced classification is compounded by properties such as dataset size, label noise, and data distribution.
Does XGBoost handle class imbalance?
The XGBoost algorithm is effective for a wide range of regression and classification predictive modeling problems. This modified version of XGBoost is referred to as Class Weighted XGBoost or Cost-Sensitive XGBoost and can offer better performance on binary classification problems with a severe class imbalance.
Does XGBoost need scaling?
Does XGBoost need scaling? Decision trees do not require normalization of their inputs; and since XGBoost is essentially an ensemble algorithm comprised of decision trees, it does not require normalization for the inputs either.
How do you reduce false positives in XGBoost?
1 Answer. Just changing the threshold of the classification changes the FP rate. I recommend you to investigate the ROC curve of your outputs.
Can XGBoost handle categorical variables?
Unlike CatBoost or LGBM, XGBoost cannot handle categorical features by itself, it only accepts numerical values similar to Random Forest. Therefore one has to perform various encodings like label encoding, mean encoding or one-hot encoding before supplying categorical data to XGBoost.
Is LightGBM better than XGBoost?
Light GBM is almost 7 times faster than XGBOOST and is a much better approach when dealing with large datasets. This turns out to be a huge advantage when you are working on large datasets in limited time competitions.
Is CatBoost faster than XGBoost?
As of CatBoost version 0.6, a trained CatBoost tree can predict extraordinarily faster than either XGBoost or LightGBM. On the flip side, some of CatBoost’s internal identification of categorical data slows its training time significantly in comparison to XGBoost, but it is still reported much faster than XGBoost.
Which is better XGBoost or CatBoost?
XGBoost is more transparent, allowing easy plotting of trees and since it has no built-in categorical features encoding, there are no possible surprises related to features type. The reason is that default CatBoost representation for categorical features is not supported by PMML.