Does correlation Affect Decision trees?
Luckily, decision trees and boosted trees algorithms are immune to multicollinearity by nature . When they decide to split, the tree will choose only one of the perfectly correlated features.
Do decision trees have high bias?
Decision Trees have extremely low bias because they maximally overfit to the training data. Each “prediction” it makes on the validation set would in essence be the fare of some taxi ride in our training data that ended up in the same final leaf node as the ride whose fare we are predicting.
Does decision tree have high bias or variance?
A complicated decision tree (e.g. deep) has low bias and high variance. The bias-variance tradeoff does depend on the depth of the tree. Decision tree is sensitive to where it splits and how it splits. Therefore, even small changes in input variable values might result in very different tree structure.
Why does feature correlation matter in decision trees?
This can lead to skewed or misleading results. Luckily, decision trees and boosted trees algorithms are immune to multicollinearity by nature. When they decide to split, the tree will choose only one of the perfectly correlated features.
Why does a decision tree have high bias and high variance?
More Bias = error from the model being more simpler (does not fit the data very well) More Variance = error from the model being more complex (fits the data too well, and learns the noise in addition to the inherent patterns in the data) I want to start by saying that everything is relative.
Why does a decision tree take the wrong branch?
The tree becomes highly tuned to the data present in the training set. But when a new data point is fed, even if one of the parameters deviates slightly, the condition will not be met and it will take the wrong branch.
How does the algorithm for a decision tree work?
The way a decision tree algorithm works is that the data is split again and again as we go down in the tree, so the actual predictions would be made by fewer and fewer data points.