Class ExtremelyRandomizedTreesRegressionOptions
Configuration options for Extremely Randomized Trees regression, an ensemble learning method that builds multiple decision trees with additional randomization for improved prediction accuracy.
public class ExtremelyRandomizedTreesRegressionOptions : DecisionTreeOptions- Inheritance
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ExtremelyRandomizedTreesRegressionOptions
- Inherited Members
Remarks
Extremely Randomized Trees (also known as Extra Trees) is an ensemble learning method that extends Random Forests by introducing additional randomization in the way splits are computed. While Random Forests search for the optimal split among a random subset of features, Extra Trees select random splits for each feature and choose the best among those. This additional randomization helps reduce variance and often improves generalization.
For Beginners: Extremely Randomized Trees is like having a large committee of decision-makers (trees) who each look at your data in a slightly different, randomized way. Imagine asking 100 people to help you decide whether to buy a house, but each person can only consider a random subset of factors (like price, location, size) and must make quick decisions without overthinking. By averaging all their opinions, you often get better advice than from just one person or from a committee that all thinks the same way. This randomness helps the model avoid focusing too much on specific patterns that might just be coincidences in your training data.
Properties
MaxDegreeOfParallelism
Gets or sets the maximum number of trees that can be trained simultaneously.
public int MaxDegreeOfParallelism { get; set; }Property Value
- int
The maximum degree of parallelism, defaulting to the number of processor cores.
Remarks
This parameter controls how many trees can be built in parallel, which can significantly speed up training on multi-core systems. By default, it uses all available processor cores. Setting this to a lower value may be beneficial on systems where you want to reserve processing power for other tasks.
For Beginners: This controls how many trees the model can build at the same time, taking advantage of multiple processor cores in your computer. By default, it uses all available cores (currently {Environment.ProcessorCount} on your system). This is like having multiple workers building different parts of a project simultaneously instead of one worker doing everything sequentially. You might lower this number if you want your computer to remain responsive for other tasks while training the model. For example, setting it to half your core count would leave processing power available for other applications.
NumberOfTrees
Gets or sets the number of decision trees to build in the ensemble.
public int NumberOfTrees { get; set; }Property Value
- int
The number of trees, defaulting to 100.
Remarks
This parameter controls the size of the ensemble. More trees generally lead to better performance but increase training time and memory usage. The performance improvement typically plateaus after a certain number of trees.
For Beginners: This controls how many different decision trees the model will create and combine for making predictions. With the default value of 100, the model builds 100 different trees and averages their predictions. Think of it like getting opinions from 100 different advisors instead of just one. More trees usually give more stable and accurate predictions (like how asking more people gives you a more reliable consensus), but they also make the model slower to train and use more memory. For simple problems, you might reduce this to 50 or even 10 trees. For complex problems where accuracy is critical, you might increase it to 200 or more.