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Avoid Overfitting in Classification The generated tree may overfit the training data Too many branches, some may reflect anomalies due to noise or outliers
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Spline has better fit because it tends to overfit. Potential subsequent tests. LOF test of spring and alternative models. LOF test of alternative and spline models ...
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Forecasting Stock Prices. Walkrich Investments: Neural ... Time (BP training, or EA/PSO generations) Training data. Validation data. Starting to overfit ...
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Recommend items based on past transactions of users. Specific data ... Can easily overfit, sensitive to regularization. Need to separate main effects...
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post-pruning fully grow the tree (allowing it to overfit the data) and then ... Nodes are pruned iteratively, always choosing the node whose removal most ...
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Decision Trees Definition Mechanism Splitting Function Issues in Decision-Tree Learning Avoiding overfitting through pruning Numeric and missing attributes
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separate test set to evaluate the use of pruning ... no bookkeeping on how to reorganize tree if root node is pruned. Improves readability ...
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Daria Sorokina. Rich Caruana. Mirek Riedewald. Daria Sorokina, Rich Caruana, Mirek Riedewald. Additive Groves of Regression Trees ...
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Cool. Weak. High. Cold. Cloudy. 6. No. Change. Warm ... Cool. Weak. Normal. Warm. Rainy. 7. 4. Decision Trees. Humidity. Normal. High. Yes. Sky. AirTemp ...
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A decision tree is a tree where each node of the tree is associated with an ... The decision trees represent a disjunction of conjunctions of constraints on the ...
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RIPPER. Fast Effective Rule Induction. Machine Learning 2003. Merlin Holzapfel & Martin Schmidt ... usually better than decision Tree learners. representable ...
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Recall = TP / (TP FN) F1 = 2Prec Rec / (Prec Rec) ICML ... CARCASS (50) 30: beef, pork, meat, dollar, chicago. SOY-MEAL (13) 3: meal, soymeal, soybean ...
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Review of : Yoav Freund, and Robert E. Schapire, 'A Short Introduction to ... Michael Collins, Discriminative Reranking for Natural Language Parsing, ICML 2000 ...
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Let S be a set examples from c classes. Where pi is the proportion of examples of S belonging ... Intuitively, the smaller the entropy, the purer the partition ...
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Discriminatively Trained Markov Model for Sequence Classification Oksana Yakhnenko Adrian Silvescu Vasant Honavar Artificial Intelligence Research Lab
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Instances are represented by discrete attribute-value pairs (though the basic ... are related as follows: the more disorderly a set, the more information is ...
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Artificial Intelligence 7. Decision trees Japan Advanced Institute of Science and Technology (JAIST) Yoshimasa Tsuruoka Outline What is a decision tree?
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Chapter 3: Decision Tree Learning Decision Tree Learning Introduction Decision Tree Representation Appropriate Problems for Decision Tree Learning Basic Algorithm ...
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Decision Tree Learning Learning Decision Trees (Mitchell 1997, Russell & Norvig 2003) Decision tree induction is a simple but powerful learning paradigm.
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Decision Trees, Nearest Neighbor/IBL, Genetic Algorithms, Rule ... Stacking, Gating/Mixture of Experts, Bagging, Boosting, Wagging, Mimicking, Combinations ...
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Biological learning system (brain) complex network of ... arrow: negated gradient at one point. steepest descent along the surface. Derivation of the rule ...
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The Models of Function Approximator. The Radial Basis ... ganglion cells. The Topology of RBF. Feature Vectors. x1. x2. xn. y1. ym. Inputs. Hidden. Units ...
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... any continuous multivariate function, to any desired degree of accuracy. ... wm. x = Single-Layer Perceptrons as. Universal Aproximators. Hidden. Units ...
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Title: PowerPoint Presentation Author: Gheorghe Tecuci Last modified by: Gheorghe Tecuci Created Date: 10/16/2000 12:50:33 AM Document presentation format
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Properties of RBF's. On-Center, Off Surround ... visual cortex; ganglion cells. The Topology of RBF. Feature Vectors. x1. x2. xn. y1. ym. Inputs ...
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borrowed some of his s for 'Neural Networks' and 'Computation in Neural Networks' courses. ... Paolo Frasconi, University of Florence. ...
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Predictive Automatic Relevance Determination by Expectation Propagation. Alan Qi. Thomas P. Minka ... Where is a cumulative distribution function for a ...
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A Tree to Predict C-Section Risk. Learned from medical records of 1000 women ... Local minima... Statistically-based search choices. Robust to noisy data...
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blond. eyes. blue. brown. Disjunction of conjunctions (one conjunct per path to a node) ... Hair: blond(2 , 2-) red(1 , 0-) dark(0 , 3 ...
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Knowledge discovery & data mining: Classification UCLA CS240A Winter 2002 Notes from a tutorial presented @ EDBT2000 By Fosca Giannotti and Dino Pedreschi
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If you use the full posterior over parameter settings, overfitting disappears! ... But it is not economical and it makes silly predictions. ...
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Research supported in part by grants from the National Science Foundation (IIS ...
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Data Mining (and machine learning) DM Lecture 6: Similarity and Distance
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Classification and Supervised Learning Credits Hand, Mannila and Smyth Cook and Swayne Padhraic Smyth s notes Shawndra Hill notes Outline Supervised Learning ...
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Weight-decay involves adding an extra term to the cost function that penalizes ... Is there a way to determine the weight-decay coefficient automatically? ...
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Parameters (weights w or a, threshold b) ... A function of the parameters of the ... Shave off unnecessary parameters of your models. The Power of Amnesia ...
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Machine Learning by Tom Mitchell, 1997, Chapter 3 ... ( chi-square test used by Quinlan at first later abandoned in favor of post-pruning) ...
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(hair = red) [(hair = blond) & (eyes = blue) ... short blond brown - 1. Find the attribute that maximizes the ... Hair: blond(2 , 2-) red(1 , 0-) dark(0 , 3 ...
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Bag CART. 4. Case study. FLDA. DLDA. DQDA. 4. Case study. 4. Case study. 4. Case study ... Bagging: 'Ipred' package. Random forest: 'randomForest' package ...
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if hair_colour='blonde' then. if weight='average' then ... blonde. red. 23 ...Calculating the Disorder of the 'blondes' The first term of the sum: D(Sblonde) ...
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Can be used to predict outcome in new situation ... To be informed of, ascertain; to receive instruction. Difficult to measure. Trivial for computers ...
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Joint work with T. Minka, Z. Ghahramani, M. Szummer, and R. W. Picard. Motivation ... Approximate a probability distribution by simpler parametric terms (Minka 2001) ...
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Risk of Overfitting by optimizing hyperparameters. Predictive ARD by expectation propagation (EP) ... of relevance or support vectors on breast cancer dataset. ...
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1. Classification and regression trees. Pierre Geurts. Stochastic methods (Prof. L.Wehenkel) ... Goal: from the database, find a function f of the inputs that ...
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Data-Oriented Parsing. Remko Scha. Institute for Logic, Language and Computation ... live on this paradoxical slope to which it is doomed by the evanescence of its ...
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Handling Continuous-Valued Attributes. Handling Missing Attribute Values. Decision Trees ... attribute as the root, create a branch for each of the values the ...
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Extension of joint work with John Langford, TTI Chicago (COLT 2004) ... Standard conversion method from to : logistic (sigmoid) transformation. For each and , set ...
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We study Bayesian and Minimum Description Length (MDL) inference in ... Our inconsistency result also holds for (various incarnations of) MDL learning algorithm ...
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Non-Symbolic AI lecture 9 Data-mining using techniques such as Genetic Algorithms and Neural Networks. Looking for statistically significant patterns hidden ...
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Examples are represented by attribute-value pairs. ... Define the classes and attributes .names file: labor-neg.names. Good, bad. Duration: continuous. ...
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If we have a big data set that needs a complicated model, the full Bayesian ... random collection of one-dimensional datapoints (except for nasty special cases) ...
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XX denotes a row with very extreme X values. Values of Predictors for New Observations ... Temperature (x1) degrees Fahrenheit. Pressure (x2) pounds per ...
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Title: CSIS 0323 Advanced Database Systems Spring 2003 Author: hkucsis Created Date: 1/18/2003 8:56:22 PM Document presentation format: On-screen Show
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... Discovery in Databases (KDD) is an emerging area that considers the process of ... composed of arbiters that are computed in a bottom-up, binary-tree fashion. ...
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Build a tree decision tree. Each node represents a test. Training instances are split at ... Fewest nodes? Which trees are the best predictors of unseen data? ...
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... greedy FS & regularization. Classical Bayesian feature ... Regularization: use sparse prior to enhance the sparsity of a trained predictor (classifier) ...
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