Table 2 Machine Learning Algorithms and their Corresponding Hyperparameters.
Model | Scikit-Learn package | Parameter name in Scikit_Learn Package | Test Range |
|---|---|---|---|
DT | tree.DecisionTreeCalssifier | max_features | [1, 10, 20, 30, 40, 50, 60, 70] |
max_depth | [1, 2] | ||
min_sample_split | [2, 5, 10] | ||
min_sample_leaf | [2, 3, 4, 5] | ||
XGBoost | xgboost.XGBClassifier | n_estimators | [100, 200, 300, 400] |
learning_rate | [0.1, 0.5, 1.0] | ||
max_depth | [1, 2] | ||
min_child_weight | [1, 3] | ||
eta | [0.8] | ||
gamma | [2] | ||
lambda | [0.5] | ||
alpha | [0.5] | ||
RF | ensemble.RandomForestClassifier | n_estimators | [100, 200, 300, 400] |
max_depth | [1,2] | ||
max_feature | [1,10,20,30,40,50,60,70] | ||
min_sample_leaf | [2,3,4, 5] | ||
min_samples_split | [2,5,10] | ||
AdaBoost | ensemble.AdaBoostClassifier | n_estimator | [100, 200, 300, 400] |
learning_rate | [0.1, 0.5, 1.0] | ||
LR | linear_model.LogisticRegression | C | [0.01,0.1,1,10] |
KNN | neighbors.KNeighborsClassifire | K | [1, 3, 5] |
NB Gaussian | naive_bayes.GaussianNB | — | — |
