Why is the variance high?
High Variance is due to a model that tries to fit most of the training dataset points making it complex. Consider the following to reduce High Variance: Reduce input features(because you are overfitting)
Why high variance is bad?
High Bias or High Variance This is bad because your model is not presenting a very accurate or representative picture of the relationship between your inputs and predicted output, and is often outputting high error (e.g. the difference between the model’s predicted value and actual value).
Is the variance of a decision tree high or low?
I want to start by saying that everything is relative. Decision Tree in general has low bias and high variance that let’s say random forests. Similarly, a shallower tree would have higher bias and lower variance that the same tree with higher depth.
How is average prediction used in decision trees?
Mathematically, the average prediction is: Applying this to decision trees, it means that that we can construct a high number of trees which will have high variance and low bias. Then, we can average their predictions to reduce the variance to improve the performance of the decision trees.
How is the variance of a random forest affected?
“You train a lot of Decision Trees on different parts of the training set and average their predictions into a final prediction. The prediction gets better, because the variance of the Random Forest is smaller compared to the variance of a single Decision Tree. (dartboard.png)” Of course, I am paraphrasing here.
How does the effect of bagging on variance and bias?
Usually, the essence is the following: “You train a lot of Decision Trees on different parts of the training set and average their predictions into a final prediction. The prediction gets better, because the variance of the Random Forest is smaller compared to the variance of a single Decision Tree. (dartboard.png)”