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Is decision tree ensemble learning?

Tiara Williamson
Tiara WilliamsonAnswered

Supervised Machine Learning approaches such as Decision Trees are used by businesses to make better choices and increase profit. Despite existing for a long time, decision trees are still susceptible to bias and variation. Simple trees have strong bias, whereas complicated trees have big variance.

The predictive performance of ensemble techniques in Machine Learning, which incorporate many decision trees, is superior to that of a single decision tree. The ensemble Machine Learning model sprouted from the idea that several weak learners may be combined to create a strong learner.

If the objective is to decrease the variance of a decision tree, bagging is used. The goal is to generate several data subsets from a training sample selected at random with replacement. Currently, each subset of data is utilized to train individual decision trees, consequently providing a collection of distinct models. The forecast average from several trees is more reliable than a single tree.

Random Forest is improved bagging. In addition to selecting a random sample of data, it also selects a random subset of features, as opposed to utilizing all features to create trees. A Random Forest is a technique that incorporates several random trees.

Advantages:

  • Handles data with greater dimensionality effectively.
  • Handles missing values and ensures data correctness despite their absence.

Boosting is another ensemble method in Machine Learning for creating a set of predictors. In this method, learners are taught progressively, with novices fitting basic models to data before examining the data for flaws. In other words, we fit successive trees (random sample) to minimize the preceding tree’s net error at each stage.

When an input is incorrectly categorized by a hypothesis, its weight is raised so that the subsequent hypothesis is more likely to identify it properly. By mixing the whole data at the end, weak learners are transformed into higher-performing models.

  • Gradient Boosting is an expansion of boosting.

To simplify, Gradient Boosting = Gradient Descent + Boosting

It employs the gradient descent technique, which can improve any differentiable loss function. An ensemble of trees is constructed one by one, and the sums of the individual trees are calculated sequentially. The following tree attempts to make up for its loss.

Pros of Gradient Boosting:

  • Supporting many loss functions
  • Effective with interactions

Cons of Gradient Boosting:

  • Susceptible to overfitting
  • Requires careful adjustment of several hyper-parameters
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