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Table 3 Summary of parameter values in each model

From: Fetal birthweight prediction with measured data by a temporal machine learning method

Models Parameters Values Parameters Meaning
SVM kernel linear kernel function
C 1.0 regularization parameter
Cache_size 200 specify the size of the kernel cache
tol 0.001 tolerance for stopping criterion
gamma scale kernel coefficient
BPNN kernel initializer uniform kernel initializer function
activation1 relu activation of hidden layer
activation2 sigmoid activation of output layer
optimizer Adam training optimization algorithm
epochs 200 number of times shown to the network
batch size 128 batch size
Linear-R fit_intercept True whether to calculate the intercept for this model
normalize False whether to standardize the data
copy_X True If True, X will be copied
CNN lr 0.01 learning rate
epochs 100 number of times shown to the network
optimizer Adam training optimization algorithm
Conv1_in_channels 1 number of channels in the input image
Conv1_outchannels 10 number of channels produced by the convolution
Conv1_kernel_size 1 size of the convolving kernel
Conv1_strid 2 stride of the convolution
Conv2_in_channels 10 number of channels in the input image
Conv2_outchannels 20 number of channels produced by the convolution
Conv2_kernel_size 1 size of the convolving kernel
Conv2_strid 2 stride of the convolution
RF n estimators 200 the number of trees in the forest
Min_samples_leaf 1 the minimum number of samples required to be at a leaf node
Min_samples_split 2 the minimum number of samples required to split an internal node
max depth None the maximum depth of the tree.
max features 7 the number of features to consider when looking for the best split
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