Contents
Why random forest is good for Imbalanced data?
Random forest is an extension of bagging that also randomly selects subsets of features used in each data sample. Both bagging and random forests have proven effective on a wide range of different predictive modeling problems.
Can decision tree handle noisy data?
Decision tree learning is one of the most commonly known machine learning algorithms out there. One of the advantages of decision trees are that there are quite staright forward, easily understandable by humans. Decision trees provide a way to approximate discrete valued functions and are robust to noisy data.
What is noisy data in decision tree?
Decision tree is one of the most popular tools in data mining and machine learning to extract useful information from stored data. However, data repositories may contain noise, which is a random error in data. The serious effect of noise is that it can confuse the learning algorithm to produce a long and complex model.
How to train a decision tree against unbalanced data?
I’m new to data mining and I’m trying to train a decision tree against a data set which is highly unbalanced. However, I’m having problems with poor predictive accuracy. The data consists of students studying courses, and the class variable is the course status which has two values – Withdrawn or Current.
How is a cost sensitive decision tree constructed?
The tree is constructed by splitting the training dataset using values for variables in the dataset. At each point, the split in the data that results in the purest (least mixed) groups of examples is chosen in a greedy manner.
How is a decision tree used in CART?
The decision tree algorithm is also known as Classification and Regression Trees (CART) and involves growing a tree to classify examples from the training dataset. The tree can be thought to divide the training dataset, where examples progress down the decision points of the tree to arrive in the leaves of the tree and are assigned a class label.
Why is an imbalanced dataset an important issue?
This is an important issue, because an imbalanced dataset may lead to inflated performance estimates. This in turn may lead to false conclusions about the significance with which the algorithm has performed better than chance. The machine-learning literature on this topic has essentially developed three solution strategies.