Decision trees
A decision tree is an ML model that is based on asking questions iteratively to split data and arrive at a solution. It’s the most natural approach to decide on an object’s classification or label. It also has the appearance of an upside-down tree with projecting branches, thus the name.
- A decision tree is a flowchart-like tree structure in which each node represents a dataset attribute, each branch represents a choice, and each leaf node represents the result.
The root node is the topmost node in a decision tree. It learns to divide based on the value of a feature. It recursively splits the tree, also known as recursive partitioning.
The decision-making process is aided by this flowchart-like layout. Its representation is similar to a flowchart diagram and readily resembles human-level thinking. As a result, decision trees are simple to comprehend and interpret.
Overfitting is a problem with decision trees, which is why they perform well on the validation dataset but poorly on the test dataset. Data scientists devised ensemble learning to address the problem faced by decision trees.
Ensemble learning
This is a model that produces predictions based on several distinct models. Ensemble learning is more flexible and less data-sensitive since it combines several distinct models. Bagging and boosting are the two most prevalent ensemble learning strategies.
- Bagging is the process of simultaneously training a large number of models. Each model is trained on a portion of the data that is chosen at random. The use of bagging may be found in Random Forests. Random forests are a series of decision trees that run in parallel. Each tree is trained on a random subset of the same data, and the classification is determined by averaging the results of all trees.
- Boosting is the process of successively training a large number of models. Each model learns from the preceding model’s errors. Gradient Boosting Decision Trees are an example of boosting in action.
Boosting is based on the notion of improving the previous student’s faults by teaching the next learner. Weak learners, who do only slightly better than a random chance, are utilized in boosting. Boosting relies on stacking up these weak learners in a progressive manner and filtering out the observations that a learner gets right at each phase. The emphasis is mostly on the development of new weak learners to manage the remaining challenging observations at each level.
Gradient Boosting Decision Trees
Gradient Boosting is a machine learning approach that may be used to solve issues in classification and regression. It is based on the idea that a group of weak learners may produce a more accurate predictor when they work together.
Gradient boosting decision trees combine several weak learners to produce a single strong learner. Individual decision trees are the poor learners in this case. All of the trees are connected in a succession, with each tree attempting to reduce the mistake of the one before it. Boosting algorithms are often difficult to train but extremely precise due to this sequential relationship. Models with a sluggish learning curve do better in statistical learning.
The weak learners are integrated in such a manner that each fresh learner integrates into the residuals of the previous stage as the model develops. The final model combines the results of each phase, resulting in a strong learner. The residuals are detected using a loss function.
In other words, gradient boosting works by repeatedly developing smaller prediction models, with each model attempting to forecast the error left over from the preceding model. As a result, the algorithm has a proclivity towards overfitting. But what exactly is a poor learning model? A model that outperforms random guesses by a small margin. I’ll show you the precise formula in a minute. However, it is necessary to first study the fundamental ideas of decision trees and ensemble learning in order to fully comprehend the underlying principles and operation of GBT.
Improve Gradient Boosting
Gradient boosting methods are prone to overfitting, resulting in poor test dataset performance. To optimize the performance of the gradient boosting technique, bear the following suggestions in mind.
- Each tree’s forecasts are combined together in order. Instead, the weighting of each tree’s contribution to this total may be used to slow down the algorithm’s learning. A shrinkage or learning rate is the term for this weighting. The performance of your gradient boosting model may be substantially improved by using a low learning rate. Bear in mind that a slow learning rate will lengthen your training time because your model will take more iterations to get a final loss value.
- Subsampling the training set and training individual learners on random samples is what stochastic gradient boosting is all about. This decreases the correlation between individual learner scores and combining low-correlation results yields a better overall result.
- Quantity of trees– Adding an excessive number of trees might result in overfitting, thus it’s crucial to stop when the loss value converges. Shorter trees are favored over more complicated trees in terms of tree depth. The number of tiers in a tree should be limited.