Tutorials - Supervised Learning & Scoring
Subject | Components | Tutorial | Dataset |
Decision Tree - ID3 Predict breast cancer from cells characteristics. |
Dataset Define Status Spv Learning (Meta Spv) ID3 |
breast | |
Discretization & Naive Bayes Classifier Supervised discretization. |
Dataset Define Status MDLPC Naive Bayes |
breast | |
Feature Selection Correlation based feature selection for supervised learning. |
Dataset Define Status MDLPC MIFS |
iris | |
Classification on a new dataset Apply a classifier on a new dataset |
Dataset Select examples Define Status C-RT View dataset Export dataset |
datasets | |
LIFT Curve Targeting potential customers [SCORING]. (CoIL Challenge -- 2000). |
Scoring Lift Spv Learning |
tic data | |
ROC Curve Computing ROC Graphs for classifier comparison. |
Scoring Roc Spv Learning |
heart | |
Use a predefined test set Compare several supervised learning algorithms on a user predefined test set. |
Spv Learning Test |
sonar data | |
Resampling Error Rate Estimate Compare supervised learning algorithm with resubstitution and cross-validation error rate estimation |
Spv Learning ID3 and K-NN Cross-Validation |
heart | |
ID3 and big dataset Supervised Learning with big dataset -- COVTYPE (581102 examples), all attributes are discrete (discretized). |
ID3 Supervised Learning |
covtype | |
Feature Construction -- SVD NIPALS, a fast SVD or PCA algorithm, useful for high dimensional dataset. Application on a proteins classification process. |
NIPALS Spv Learning K-NN Bootstrap |
dataset | |
SVM SVM -- Support Vector Machine. A supervised learning algorithm which is well adapted for high dimensional problems. Implements John C. Platt's sequential minimal optimization algorithm for training a support vector classifier using polynomial or RBF kernels. References J. Platt (1998). Fast Training of Support Vector Machines using Sequential Minimal Optimization. Advances in Kernel Methods - Support Vector Learning, B. Schölkopf, C. Burges, and A. Smola, eds., MIT Press. S.S. Keerthi, S.K. Shevade, C. Bhattacharyya, K.R.K. Murthy, Improvements to Platt's SMO Algorithm for SVM Classifier Design. Neural Computation, 13(3), pp 637-649, 2001. Nota: This is a port of WEKA implementation (SMO.JAVA, ver. 3-4) |
SVM Spv Learning Bootstrap |
sonar | |
Classification Trees and Decision Lists Compare Decision Lists and Decision Trees algorithms on HEART dataset. These methods give similar results. |
MDLPC Decision List Spv Learning Bootstrap |
heart | |
SVM for classification task C-SVC, a very efficient implementation of a multi-class SVM from the LIBSVM library. |
SVM Spv Learning Bootstrap |
protein classification | |
Random Forest Supervised learning with Breiman's Random Forest. |
BAGGING Random Tree |
heart | |
STEPDISC Stepwise Discriminant Analysis. Feature selection for Linear Discriminant Analysis. |
Stepdisc Linear Discriminant Analysis |
sonar | |
FORWARD/BACKWARD LOGIT Variable selection for binary logistic regression. |
Forward-logit Backward-logit Scoring Lift curve Binary logistic regression |
bank | |
MULTINOMIAL LOGISTIC REGRESSION Multinomial logistic regression (or polytomous logistic regression for nominal dependent variable). |
Multinomial Logistic Regression | brand | |
Partial Least Squares Discriminant Analysis Using the PLS Regression principle for classification task. |
C-PLS PLS-DA PLS-LDA |
breast |