Decision tree algorithm short Weka tutorial Machine Learning : brief summaryDanilo, CroceBasili, Roberto
3. Patel N, Upadhyay S. Study of various decision tree pruning methods with their empirical comparison in WEKA.Int J Comp Appl.60(12):20–25. [Google Scholar] 4. Berry MJA, Linoff G.Mastering Data Mining: The Art and Science of Customer Relationship Management.New York: John Wiley & S...
1:最优Decision Tree是NP难题,所以使用的Decision-Tree算法都是基于启发式(Heuristic)算法,如Greedy Algorithm等,在每个节点判断都是根据局部最优解来进行操作。启发式算法不能保证返回全局最优的Decision Tree。 2:容易产生过于复杂的树,不能很好地获得数据的通用模型,这个实际上是被称为是Overfitting,剪枝技术能够很好...
In Weka, the decision tree analysis is in the Classify tab page (Figure 4), and the decision tree algorithm can be selected by clicking the Choose option on the Classify tab page. In the options of the decision tree algorithm of Weka, the J48 algorithm is the C4.5 decision tree algorithm...
The Decision Tree Algorithm A decision tree is a flowchart-like tree structure where an internal node represents a feature(or attribute), the branch represents a decision rule, and each leaf node represents the outcome. The topmost node in a decision tree is known as the root node. It learns...
Classification And Regression Tree(CART)也是决策树的一种,并且是非常重要的决策树。除去上文提到的C4.5外,CART算法也在Top Ten Machine Learning Algorithm中,可见决策树的重要性和CART算法的重要性。 CART的特性主要有下面三个,其实这些特性都不完全算是CART的特性,只是在CART算法中使用,并且作为算法的重要基础: ...
Classification of bruised apples 400–1000 Savitzky–Golay method (second derivative) Otsu thresholding algorithm Logistic model tree (LMT) Minimum number of instances:15;Number of Boosting Iterations: −1 Ten-fold 83:17 WEKA 98% Baranowski et al. (2013) Detection of Marssonina blotch in appl...
Decision tree algorithm is a heuristic greedy algorithm, and its tree-building process also shows a “divide and conquer” idea. When generating a decision tree, if all instances in a node belong to the same class, it is set as a leaf node; otherwise, an optimal attribute is selected as...
We experimentally test RSMDT algorithm in terms of classification accuracy, tree size and computing time, using the whole 36 UCI Machine Learning Repository data sets selected by Weka platform, and compare it with C4.5, classification and regression trees (CART), classification and regression trees ...
For example, the decision tree–building algorithm does not handle numeric attributes uniformly. When applying the numeric attributes to generate the decision tree, numeric attributes may be used more than once with different thresholds. Some im- portant attributes are excluded in the data set; thus...