Machine Learning Models

Hyperparameter

Boosted Random Forest

n estimators

max depth

criterion

min samples split

min samples leaf

max features

Decision Tree

criterion

max depth

min samples split

min samples leaf

max features

SVM

C

kernel

KNN

N neighbors

MLP

ANN

number of hidden

layers,

loss,

optimizer,

activation,

learning rate,

dropout rate,

epochs,

batch size,

early stop patience