'Forward selection' diaporamas de présentation

Data Preprocessing

Data Preprocessing. Data cleaning Data integration and transformation Data reduction Discretization and concept hierarchy generation Summary. Multi-Dimensional Measure of Data Quality. A well-accepted multidimensional view: Accuracy Completeness Consistency Timeliness

1.18k views • 48 slides

Feature Selection, Feature Extraction

Feature Selection, Feature Extraction. Need for reduction. Classification of leukemia tumors from microarray gene expression data 1 72 patients (data points) 7130 features (expression levels of different genes) Text mining, document classification features are words

1.86k views • 42 slides

STATS 330: Lecture 15

506 views • 38 slides

Lecture 3: Embedded methods

Lecture 3: Embedded methods. Isabelle Guyon isabelle@clopinet.com. Filters,Wrappers, and Embedded methods. Feature subset. All features. Filter. Predictor. Multiple Feature subsets. All features. Predictor. Feature subset. Embedded method. All features. Wrapper. Predictor. Filters.

437 views • 35 slides

Model Selection

419 views • 27 slides

380 views • 17 slides

All Possible Regressions and Statistics for Comparing Models

All Possible Regressions and Statistics for Comparing Models . Animal Science 500 Lecture No. 12 October 12, 2010. Example analysis. The RSQUARE Procedure RECALL The RSQUARE procedure selects optimal subsets of independent variables in a multiple regression analysis. Example analysis.

260 views • 16 slides

JMP Example 7

JMP Example 7. Build a model using JMP data table stat_7_data with potential factors: temperature, A/B feed ratio , and Termination time and the response variable yield. Determine which terms are significant. Build the model using forward and backward selection technique.

233 views • 14 slides

Computational Intelligence: Methods and Applications

Computational Intelligence: Methods and Applications. Lecture 34 Applications of information theory and selection of information W?odzis?aw Duch Dept. of Informatics, UMK Google: W Duch. Some applications of info theory.

124 views • 12 slides

Multiple Linear Regression

Multiple Linear Regression. (MLR). Three General Techniques for Model Building. Three General Techniques for Model Building. All possible regressions. Forward selection. Backward elimination. With k = 2 independent variables there are four possible regression models:. 1) 2) 3) 4).

206 views • 9 slides

Statistics 350 Lecture 26

82 views • 8 slides

Landscape and environmental factors associated with BMSB

Landscape and environmental factors associated with BMSB. Stepwise Regression (forward selection ? = 0.05) with 67 total variables 21 temperature, 4 topographical, 4 climatic 38 multi-scale land use variables.

108 views • 1 slides

Prediction of Malignancy of Ovarian Tumors Using Least Squares Support Vector Machines

Positive definite kernel K(.,.) RBF: Linear:. Procedure of developing models to predict the malignancy of ovarian tumors. ROC curve on test set. Expected ROC curve on validation. training. test. -- LSSVMrbf -- LSSVMlin -- LR -- RMI.

121 views • 1 slides