Table of Contents
What is REPTree in Weka?
” Reduced Error Pruning Tree (RepTree) is a fast decision tree learner that builds a decision/regression tree using information gain as the splitting criterion, and prunes it using reduced error pruning algorithm.”
What is pre-pruning?
Pre-pruning, also known as Early Stopping Rule, is the method where the subtree construction is halted at a particular node after evaluation of some measure. These measures can be the Gini Impurity or the Information Gain. In pre-pruning, we evaluate the pruning condition based on the above measures at each node.
What is pre-pruning in data mining?
In the pre-pruning approach, a tree is “pruned” by labored its construction early (e.g., by determining not to further divide or partition the subset of training samples at a provided node). Upon halting, the node turns into a leaf.
What is tree pruning explain with example?
Pruning is a data compression technique in machine learning and search algorithms that reduces the size of decision trees by removing sections of the tree that are non-critical and redundant to classify instances.
What is stump in decision tree?
Definition. A decision stump is a Decision Tree, which uses only a single attribute for splitting. For discrete attributes, this typically means that the tree consists only of a single interior node (i.e., the root has only leaves as successor nodes). If the attribute is numerical, the tree may be more complex.
What is pruning explain?
pruning, in horticulture, the removal or reduction of parts of a plant, tree, or vine that are not requisite to growth or production, are no longer visually pleasing, or are injurious to the health or development of the plant.
What is the difference between pre pruning and post pruning?
As the names suggest, pre-pruning or early stopping involves stopping the tree before it has completed classifying the training set and post-pruning refers to pruning the tree after it has finished.
What is the difference between pre-pruning and post-pruning?
Which one is better pre or post-pruning?
Compared to post-pruning, pre-pruning is faster. This is especially important on larger (either more features or more data) datasets where post-pruning has to evaluate a very large subset of trees.
Why is pruning used for?
Pruning is when you selectively remove branches from a tree. The goal is to remove unwanted branches, improve the tree’s structure, and direct new, healthy growth.
What is the difference between decision tree and decision stump?
A decision stump is a Decision Tree, which uses only a single attribute for splitting. For discrete attributes, this typically means that the tree consists only of a single interior node (i.e., the root has only leaves as successor nodes). If the attribute is numerical, the tree may be more complex.
What is Gini index in decision tree?
It is calculated by subtracting the sum of squared probabilities of each class from one. It favors larger partitions and is easy to implement, whereas information gain favors smaller partitions with distinct values. A feature with a lower Gini index is chosen for a split.
What are the two types of pruning?
There are two basic types of pruning cuts, heading and thinning (Fig.
What is Rule post pruning?
Infer decision tree from training set. Convert tree to rules – one rule per branch. Prune each rule by removing preconditions that result in improved estimated accuracy. Sort the pruned rules by their estimated accuracy and consider them in this sequence when classifying unseen instances.