Data Mining and Big Data by Unknown

Author:Unknown
Language: eng
Format: epub
ISBN: 9789811572050
Publisher: Springer Singapore

4.5 Model Importance Interpretation

This paper uses the common algorithm xgboost in boosting, and compares and verifies the feature importance obtained from the random forest algorithm in bagging. Since the GBDT algorithm only has a regression tree, it will not be discussed here. This adjusted random forest model consists of 50 lessons of decision trees. Each tree can get an impurity measure about each feature, and then the scores can be added according to the feature to get the relevant feature importance [1]. At the same time, we use the xgboost algorithm, which is also composed of 50 decision trees, to compare. After adjusting the parameters, the optimal subsample is 0.5204081. The best learning_rate is 0.3000012, the import_type is modified to weight, and the objective is modified to multisoftprob. After completion, we can get the feature importance corresponding to the two algorithms, see Fig. 5 below.

As shown in Fig. 5, assuming that the sum of the importance of all features is 1, it can be seen that in two well-known algorithms, the importance of Inbound Tourism numbers is the largest, indicating that the largest factor affecting the regional economy is Inbound Tourism numbers during the entire classification This indicator is followed by GDP. Among them, aggregate demand index and tax cuts subsidies and Air Cargo Loaded and other corresponding features account for a small proportion, indicating that in the process of economic development, the impact of these factors is small. We can draw from this model that Singapore can vigorously develop the tourism industry and prompt the corresponding GDP, which needs to be strengthened on the characteristics of lower scores.

Fig. 5.Compare results

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