Skip to main content
Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

< Back to Article

Fig 1.

BP neural network model structure.

More »

Fig 1 Expand

Table 1.

P2P lending borrower credit risk assessment indicators.

More »

Table 1 Expand

Fig 2.

P2P lending borrower credit risk assessment model.

More »

Fig 2 Expand

Fig 3.

Log-Sigmoid function.

More »

Fig 3 Expand

Fig 4.

Tan-Sigmoid function.

More »

Fig 4 Expand

Fig 5.

BP neural network structure (LM).

More »

Fig 5 Expand

Table 2.

Comparison of prediction with different hidden Layer Nodes (LM).

More »

Table 2 Expand

Fig 6.

Trainlm performance graph.

More »

Fig 6 Expand

Fig 7.

BP neural network structure (SCG).

More »

Fig 7 Expand

Table 3.

Comparison of prediction with different hidden layer nodes (SCG).

More »

Table 3 Expand

Fig 8.

Trainscg performance graph.

More »

Fig 8 Expand

Fig 9.

BP neural network structure (BR).

More »

Fig 9 Expand

Table 4.

Comparison of prediction with different hidden layer nodes (BR).

More »

Table 4 Expand

Fig 10.

Trainbr performance graph.

More »

Fig 10 Expand

Fig 11.

ROC curve.

More »

Fig 11 Expand

Fig 12.

Confusion matrix.

More »

Fig 12 Expand

Table 5.

Comparison of validation.

More »

Table 5 Expand

Table 6.

P2P lending borrower credit risk assessment indicators.

More »

Table 6 Expand

Fig 13.

Exploratory data analysis for age (A2).

More »

Fig 13 Expand

Fig 14.

Exploratory data analysis for loan amount (A10).

More »

Fig 14 Expand

Fig 15.

Exploratory data analysis for loan term (A12).

More »

Fig 15 Expand

Fig 16.

Exploratory data analysis for credit limit (A22).

More »

Fig 16 Expand

Table 7.

Compared the BP model with competitive models.

More »

Table 7 Expand

Fig 17.

First read Excel data.

More »

Fig 17 Expand

Fig 18.

Data standardization.

More »

Fig 18 Expand