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Table 2 Key features of algorithms used

From: How much do social connections matter in fundraising outcomes?

MLP Decision tree LR Random forest
Feed data to input layer
Set connection factor w, offset b in hidden layer
Use Sigmoid function as activation function
Output classification results
Compute information gain for each variable
Select the variable with largest information gain as the root node
Repeat above steps until tree is built
Construct prediction function, using Eq. (3)
Construct loss function J(θ)
Using gradient descent to find smallest J(θ)
Random sampling Split nodes
Repeat step 2 until no more splits
Repeat steps 1 to 3 to build decision trees to form a random forest
Bayesian inference KNN AdaBoost SVM
Set a priori probability
Set conditional probability from the given information
Transform a priori probability into posterior probability with the information
Randomly select n nodes as classification centres
Compute distance from each node to the centres
Update centres
Repeat steps 1 to 3 until convergence
Initialize weight distribution D1
Train weak classifier
Combine the trained weak classifiers into a strong classifier
Find number of classifiers
Apply kernel function
Train data to obtain hyperplane
Repeat steps 1 to 3 until convergence