Issues in Decision Tree Learning and How-To solve them – Part 2
- REDUCED ERROR PRUNING. Reduced-error pruning is a method that considers each of the decision nodes in the tree as a...
- Rule Post-Pruning. One rule is produced for each leaf node in the tree during rule post pruning. Each attribute test...
- Continuous-Valued Attributes Incorporation. Our original definition of...
- Instances are represented by attribute-value pairs.
- The target function has discrete output values.
- Disjunctive descriptions may be required.
- The training data may contain errors.
- The training data may contain missing attribute values.
- Suitable for classification.
What is the problem of learning an optimal decision tree?
The problem of learning an optimal decision tree is known to be NP-complete under several aspects of optimality and even for simple concepts. Consequently, practical decision-tree learning algorithms are based on heuristics such as the greedy algorithm where locally optimal decisions are made at each node.
Are decision trees biased in favor of attributes with more levels?
For data including categorical variables with different numbers of levels, information gain in decision trees is biased in favor of attributes with more levels. However, the issue of biased predictor selection is avoided by the Conditional Inference approach, a two-stage approach, or adaptive leave-one-out feature selection.
What is a decision tree in machine learning?
The decision tree is one of the popular machine learning techniques used in the industry. It works on the principle of the “if-then” paradigm. In simpler terms, it is a set of questions asked by the model to the data. If the answer is “Yes” then there will be a set of sub-questions or defined output that will be considered as a prediction.
How can we improve the performance of decision trees?
Use all the available data for training, but apply a statistical test to estimate whether expanding (or pruning) a particular node is likely to produce an improvement beyond the training set Use measure of the complexity for encoding the training examples and the decision tree, halting growth of the tree when this encoding size is minimized.
What are the issues of decision tree learning in machine learning?
The weaknesses of decision tree methods : Decision trees are less appropriate for estimation tasks where the goal is to predict the value of a continuous attribute. Decision trees are prone to errors in classification problems with many classes and a relatively small number of training examples.
What is one of the main problems of the decision tree?
Decision-tree learners can create over-complex trees that do not generalize the data well. This is called overfitting. Decision trees can be unstable because small variations in the data might result in a completely different tree being generated.
What are the problems a decision tree solves?
They can be used to solve both regression and classification problems. Decision tree uses the tree representation to solve the problem in which each leaf node corresponds to a class label and attributes are represented on the internal node of the tree.
What are the issues in decision tree learning and how are they overcome?
Determining how deep to grow the decision tree, handling continuous attributes, choosing an appropriate attribute selection measure, and handling training data with missing attribute values, handling attributes with different costs, and improving computational efficiency are all practical issues in learning decision ...
Which of the following is a Limitations of decision trees?
One of the limitations of decision trees is that they are largely unstable compared to other decision predictors. A small change in the data can result in a major change in the structure of the decision tree, which can convey a different result from what users will get in a normal event.
Which one is disadvantage of decision tree algorithm?
Disadvantage: A small change in the data can cause a large change in the structure of the decision tree causing instability. For a Decision tree sometimes calculation can go far more complex compared to other algorithms. Decision tree often involves higher time to train the model.
Which of the following are the disadvantage of decision tree algorithm?
Decision Tree is prone to overfit and accuracy doesn't help to generalize.Information gain is more stable as compared to accuracy.Information gain chooses more impactful features closer to root.All of these.
What is decision tree explain its advantages and disadvantages?
They are very fast and efficient compared to KNN and other classification algorithms. Easy to understand, interpret, visualize. The data type of decision tree can handle any type of data whether it is numerical or categorical, or boolean. Normalization is not required in the Decision Tree.
What are decision trees used for?
Decision Trees (DTs) are a non-parametric supervised learning method used for classification and regression. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features.
What is a good example of using decision trees?
A decision tree is a very specific type of probability tree that enables you to make a decision about some kind of process. For example, you might want to choose between manufacturing item A or item B, or investing in choice 1, choice 2, or choice 3.
What is decision tree in financial management?
Decision trees are organized as follows: An individual makes a big decision, such as undertaking a capital project or choosing between two competing ventures. These decisions, which are often depicted with decision nodes, are based on the expected outcomes of undertaking particular courses of action.
What is decision tree analysis?
Decision tree analysis involves visually outlining the potential outcomes, costs, and consequences of a complex decision. These trees are particularly helpful for analyzing quantitative data and making a decision based on numbers.
What are appropriate problems for Decision tree learning?
Although a variety of decision tree learning methods have been developed with somewhat differing capabilities and requirements, decision tree learning is generally best suited to problems with the following characteristics:
Summary
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What is decision tree learning?
Decision tree learning or induction of decision trees is one of the predictive modelling approaches used in statistics, data mining and machine learning. It uses a decision tree (as a predictive model) to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves).
What is decision tree?
Decision trees are among the most popular machine learning algorithms given their intelligibility and simplicity. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making.
What is a Cart tree?
The term classification and regression tree (CART) analysis is an umbrella term used to refer to either of the above procedures, first introduced by Breiman et al. in 1984. Trees used for regression and trees used for classification have some similarities – but also some differences, such as the procedure used to determine where to split.
What is conditional inference tree?
Conditional Inference Trees. Statistics-based approach that uses non-parametric tests as splitting criteria, corrected for multiple testing to avoid overfitting. This approach results in unbiased predictor selection and does not require pruning.
How to build a tree with a goodness?
To build the tree, the "goodness " of all candidate splits for the root node need to be calculated. The candidate with the maximum value will split the root node, and the process will continue for each impure node until the tree is complete.
What is classification tree analysis?
Classification tree analysis is when the predicted outcome is the class (discrete) to which the data belongs.
How many internal nodes are there in a decision tree?
A special case of a decision tree is a decision list, which is a one-sided decision tree, so that every internal node has exactly 1 leaf node and exactly 1 internal node as a child (except for the bottommost node, whose only child is a single leaf node).
What is decision tree learning?
Decision Tree Learning is a mainstream data mining technique and is a form of supervised machine learning. A decision tree is like a diagram using which people represent a statistical probability or find the course of happening, action, or the result. A decision tree example makes it more clearer to understand the concept.
What is decision tree?
A decision tree is one of the popular as well as powerful tools which is used for prediction and classification of the data or an event. It is like a flowchart but having a structure of a tree.
How old is Jonas in Decision Tree?
From the data given let’s take Jonas’ example to check if the decision tree is classified correctly and if it predicts the response variable correctly. Jonas is not a smoker, is a drinker, and weighs under 90 kg. According to the decision tree, he will die old (age at which he dies>70). Also, according to the data, he died when he was 88 years old, this means the decision tree example has been classified correctly and worked perfectly.
How does a decision tree work?
But did you ever wonder about the basic idea behind the working of a decision tree? In a decision tree, the set of instances is split into subsets in a manner that the variation in each subset gets smaller. That is, we want to reduce the entropy, and hence, the variation is reduced and the event or instance is tried to be made pure.
Why are decision trees important?
The decision trees are also helpful in identifying possible options and weighing the rewards and risks against each course of action that can be yielded. A decision tree is deployed in many small scale as well as large scale organizations as a sort of support system in making decisions.
What is the worst case scenario for entropy?
Consider the case when we don’t have people split into any category. It is a worst-case scenario (high entropy) when both types of people have the same amount. The ratio here is 3:3.
Can you span a decision tree?
To conclude your tree properly, you can span it as short or as long as needed depending on the event and the amount of data. Let us take a simple decision tree example to understand it better.
Avoiding Overfitting The Data
Incorporating Continuous-Valued Attributes
- Our initial definition of ID3 is restricted to attributes that take on a discrete set of values. 1. The target attribute whose value is predicted by learned tree must be discrete valued. 2. The attributes tested in the decision nodes of the tree must also be discrete valued. This second restriction can easily be removed so that continuous-valued de...
Alternative Measures For Selecting Attributes
- There is a natural bias in the information gain measure that favors attributes with many values over those with few values. 1. As an extreme example, consider the attribute Date, which has a very large number of possible values. What is wrong with the attribute Date? Simply put, it has so many possible values that it is bound to separate the training examples into very small subsets. …
Handling Missing Attribute Values
- In certain cases, the available data may be missing values for some attributes. For example, in a medical domain in which we wish to predict patient outcome based on various laboratory tests, it may be that the Blood-Test-Result is available only for a subset of the patients. In such cases, it is common to estimate the missing attribute value based on other examples for which this attribut…
Handling Attributes with Differing Costs
- In some learning tasks the instance attributes may have associated costs. For example, in learning to classify medical diseases we might describe patients in terms of attributes such as Temperature, BiopsyResult, Pulse, BloodTestResults, etc. These attributes vary significantly in their costs, both in terms of monetary cost and cost to patient comfort. In such tasks, we would …
Reduced Error Pruning
- Reduced-error pruning is a method that considers each of the decision nodes in the tree as a candidate for pruning. Pruning a decision node entails deleting the subtree rooted at that node, converting it to a leaf node, and assigning it the most common categorization of the training instances associated with it. Only nodes are deleted if the result...
Rule Post-Pruning
- One rule is produced for each leaf node in the tree during rule post pruning. Each attribute test along the route from the root to the leaf is a rule antecedent (precondition), and the categorization at the leaf node is the rule consequent (postcondition). 1. Infer the decision tree from the training data, expanding the tree until the training data is as well fitted as feasible while allowing for over…
Continuous-Valued Attributes Incorporation
- Our original definition of ID3 is limited to characteristics with a finite number of possible values. First, the discrete value of the target attribute whose value is anticipated by the learned tree is required. Second, the properties examined in the tree’s decision nodes must have discrete values as well. This second constraint may be readily relaxed, allowing continuous-valued decision char…
Alternative Attribute Selection Measures
- The information gain measure has an inherent bias that favors qualities with many values over those with few. Consider the property Date, which has a huge range of potential values (e.g., March 4, 1979). This characteristic would have the biggest information gain of all of the attributes if it were added to the data. This is due to the fact that across the training data, Date alone fully …
Handling Cost-Differentiated Attributes
- The instance properties may have costs associated with them in some learning tasks. For example, when learning to categorize medical conditions, we may use phrases like Temperature, BiopsyResult, Pulse, BloodTestResults, and so on to characterize patients. These characteristics have a wide range of expenses, both monetary and in terms of patient comfort. We would like de…