Search results
Aug 29, 2017 · Classification is a data mining (machine learning) technique used to predict group membership for data instances. There are several classification techniques that can be used for...
- Help Center
Help Center - (PDF) Classification Techniques in Machine...
- Help Center
Classification. Using data to build models and make predictions. Supervised. Training data, each example: Set of predictor feature values values – numeric - or categorical. • Categorical Numerical output value - “dependent “label” variable”.
Linear classification: simple approach Figure borrowed from Pattern Recognition and Machine Learning, Bishop Drawback: not robust to “outliers”
- 1MB
- 34
- 4.2 General Approach to Solving a Classification Problem
- 4.3 Decision Tree Induction
- 4.3.1 How a Decision Tree Works
- Design Issues of Decision Tree Induction
- 4.3.6 An Example: Web Robot Detection
- 4.3.7 Characteristics of Decision Tree Induction
- Incorporating Model Complexity
- Using a Validation Set
- 4.4.5 Handling Overfitting in Decision Tree Induction
- 4.5 Evaluating the Performance of a Classifier
- 4.5.1 Holdout Method
- 4.5.2 Random Subsampling
- 4.5.3 Cross-Validation
A classification technique (or classifier) is a systematic approach to building classification models from an input data set. Examples include decision tree classifiers, rule-based classifiers, neural networks, support vector machines, and na ̈ıve Bayes classifiers. Each technique employs a learning algorithm to identify a model that best fits the ...
This section introduces a decision tree classifier, which is a simple yet widely used classification technique.
To illustrate how classification with a decision tree works, consider a simpler version of the vertebrate classification problem described in the previous sec-tion. Instead of classifying the vertebrates into five distinct groups of species, we assign them to two categories: mammals and non-mammals. Suppose a new species is discovered by scientists...
learning algorithm for inducing decision trees must address the following two issues. How should the training records be split? Each recursive step of the tree-growing process must select an attribute test condition to divide the records into smaller subsets. To implement this step, the algorithm must provide a method for specifying the test condit...
Web usage mining is the task of applying data mining techniques to extract useful patterns from Web access logs. These patterns can reveal interesting characteristics of site visitors; e.g., people who repeatedly visit a Web site and view the same product description page are more likely to buy the product if certain incentives such as rebates or f...
The following is a summary of the important characteristics of decision tree induction algorithms. Decision tree induction is a nonparametric approach for building classifi-cation models. In other words, it does not require any prior assumptions regarding the type of probability distributions satisfied by the class and other attributes (unlike some...
As previously noted, the chance for model overfitting increases as the model becomes more complex. For this reason, we should prefer simpler models, a strategy that agrees with a well-known principle known as Occam’s razor or the principle of parsimony: Definition 4.2. Occam’s Razor: Given two models with the same general-ization errors, the simple...
In this approach, instead of using the training set to estimate the generalization error, the original training data is divided into two smaller subsets. One of the subsets is used for training, while the other, known as the validation set, is used for estimating the generalization error. Typically, two-thirds of the training set is reserved for mo...
In the previous section, we described several methods for estimating the gen-eralization error of a classification model. Having a reliable estimate of gener-alization error allows the learning algorithm to search for an accurate model without overfitting the training data. This section presents two strategies for avoiding model overfitting in the ...
Section 4.4.4 described several methods for estimating the generalization error of a model during training. The estimated error helps the learning algorithm to do model selection; i.e., to find a model of the right complexity that is not susceptible to overfitting. Once the model has been constructed, it can be applied to the test set to predict th...
In the holdout method, the original data with labeled examples is partitioned into two disjoint sets, called the training and the test sets, respectively. A classification model is then induced from the training set and its performance is evaluated on the test set. The proportion of data reserved for training and for testing is typically at the dis...
The holdout method can be repeated several times to improve the estimation of a classifier’s performance. This approach is known as random subsampling. Let acci be the model accuracy during the ith iteration. The overall accuracy is given by k accsub = i=1 acci/k. Random subsampling still encounters some of the problems associated with the holdout ...
An alternative to random subsampling is cross-validation. In this approach, each record is used the same number of times for training and exactly once for testing. To illustrate this method, suppose we partition the data into two equal-sized subsets. First, we choose one of the subsets for training and the other for testing. We then swap the roles ...
- 440KB
- 61
Abstract: Classification is a data mining (machine learning) technique used to predict group membership for data instances. There are several classification techniques that can be used for classification purpose. In this paper, we present the basic classification techniques.
- 268KB
- 7
This chapter presents the main classic machine learning (ML) algorithms. There is a focus on supervised learning methods for classification and re-gression, but we also describe some unsupervised approaches. The chap-ter is meant to be readable by someone with no background in machine learning.
People also ask
What is classification in machine learning?
What are supervised machine learning classification techniques?
What are the different types of machine learning algorithms?
What is classification in data mining?
Jun 8, 2017 · This paper describes various Supervised Machine Learning (ML) classification techniques, compares various supervised learning algorithms as well as determines the most efficient...
