![]() ![]()
For example, we can see that the most important feature in predicting authenticity here is the feature BOTTOM. A higher value of this metric when compared to another feature implies it is more important for generating a prediction. The Gain metric corresponds to the relative contribution of a feature to the model. Next, we can look at the Variable importance table chart to see which variables are most important in predicting banknote authenticity. The Results by object table contains for each observation, the real class, the predicted class, and the probability to belong to the different categories of the response variable. Thus, we can see that the surfaces of the squares completely overlap for the two modalities (79 well-predicted observations out of 82 observed observations for modality 0 and 87 well-predicted observations out of 88 observed observations for modality 1). The orange squares represent the predicted numbers for each modality. The gray squares on the diagonal represent the observed numbers for each modality. The confusion plot allows visualizing this table in a synthetic way. For example, we can see that the observations of modality 0 (genuine) were well classified at 96.34% while the observations of modality 1 (counterfeit ) were well classified at 98.86%. This table shows the percentage of observations that were well classified for each modality (true positives and true negatives). The confusion matrix for the training sample is then displayed in the report. Here, the misclassification rate is 2,4% for the training and 6,7% for the validation set. The misclassification rates give an indication of how well the model performs both on the training and validation set. Go to the Outputs tab and activate the confusion matrix, Variable importance with importance type set to Gain and Results by objects to display the corresponding results.Ĭlick on OK to perform the calculations and display the results. In the Validation tab, choose to keep 30 observations at random to be able to test the model performance on new data. In the Options tab, many parameters are available to set up the model. In the quantitative explanatory variables select all the remaining variables in the dataset. Set the Response type to binary because the response variable has only two distinct values. In the Response variable field, select the "Counterfeit" variable. Select the XLSTAT/ Machine learning / Extreme Gradient Boosting. Length, Left, Right, Bottom, Top are quantitative variables. 100 banknotes are counterfeits when the 100 others are genuine in this dataset. The “Banknotes” dataset comprises a list of 200 banknotes with some information: This dataset contains 6 variables One is qualitative and concerns the banknotes' authenticity and the others are quantitative and related to banknotes' shapes.Ĭounterfeit: in case the banknote is genuine we put “0”, in the contrary case the banknote is counterfeit we put “1”. The dataset used in this tutorial is extracted from the data science platform, Kaggle and might be accessed at this address. Dataset for setting up a Gradient Boosting model (XGBOOST) Xlstat tutorial how to#Selecting data with XLSTAT demoSelect.xls Although the data selection with XLSTAT is very intuitive for MS Excel users, some of you who.This tutorial will show you how to set up and train an XGBOOST classifier in Excel using the statistical software XLSTAT. Xlstat tutorial pdf#PDF Printer Free PDF converter Free Backup software. Render PDF files within applications that support the print. XLSTAT complete analysis and statistics add-in for MS Excel. Installation or operation not performed with these.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |