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We will compare the best results of the raw data with CHI and PCA.Compared to the raw data, CHI and CHI-PCA improved in the computations of LOG, MPC, NB. However, the performance decreased with DT, and GBT.CHI-PCA-NB presented the worst value, with an accuracy of 68.4%, an F1 score of 75.7%, an MCC of 37.7%, and a Kappa of 33.7%.Cleveland dataset obtained the best results using CHI-PCA (Fig.GBT presented a pattern when applied with CHI and PCA as shown in Fig.2 and Fig. 3).2.
We will compare the best results of the raw data with CHI and PCA. For CHI, we selected 13 features. PCA created the principal components using the same CHI features. Cleveland dataset obtained the best results using CHI-PCA (Fig. 2 and Fig. 3). Nevertheless, DT and GBT presented better results using raw data. Compared to the raw data, CHI and CHI-PCA improved in the computations of LOG, MPC, NB. However, the performance decreased with DT, and GBT. The greatest improvement was in MPC using the features of CHI-PCA. MPC had an 8.1% accuracy increase, and an F1 score of 9.1%, respectively. RF behavior was the same with raw data and CHI, computing a recall of 100%, an accuracy of 98.9%, an F1 score of 98.8%, an MCC of 97.7%, and a Kappa of 97.7%. CHI-PCA-NB presented the worst value, with an accuracy of 68.4%, an F1 score of 75.7%, an MCC of 37.7%, and a Kappa of 33.7%. GBT presented a pattern when applied with CHI and PCA as shown in Fig. 2. The values of MCC and Kappa are consistent among them.
Summarize English and Arabic text using the statistical algorithm and sorting sentences based on its importance
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