Table 4 Hyper parameter analysis of various ML models.
From: Reliable water quality prediction and parametric analysis using explainable AI models
Model | Parameters | Values |
|---|---|---|
Logistic regression | \(\bullet\) Penalty | \(\bullet\) None |
\(\bullet\) Dual | \(\bullet\) False | |
\(\bullet\) Tolerance | \(\bullet\) Default | |
\(\bullet\) Regularization Strength | \(\bullet\) Default =1.0 | |
\(\bullet\) Fit_Intercept | \(\bullet\) True (Boolean) | |
\(\bullet\) Class_Weight | \(\bullet\) True(Boolean) | |
\(\bullet\) Random_state | \(\bullet\) 0 (Default) | |
\(\bullet\) Solver | \(\bullet\) lbfgs(Default) | |
SVM | \(\bullet\) C | \(\bullet\) 1,0 |
\(\bullet\) Kernal | \(\bullet\) Linear | |
\(\bullet\) Degree | \(\bullet\) 3 | |
\(\bullet\) Gamma | \(\bullet\) Scale | |
\(\bullet\) Co_ef | \(\bullet\) 0 | |
\(\bullet\) Shrinking | \(\bullet\) True(Boolean) | |
\(\bullet\) Tolerance | \(\bullet\) False(Boolean) | |
\(\bullet\) Cache | \(\bullet\) Default | |
\(\bullet\) Class_weight | \(\bullet\) 200MB | |
\(\bullet\) Verbrose | \(\bullet\) None | |
\(\bullet\) Maximum_Iteration | \(\bullet\) False | |
\(\bullet\) decision_function_shape | \(\bullet\) 1 | |
\(\bullet\) break_ties | \(\bullet\) ovr(one vs rest) | |
\(\bullet\) random_state | \(\bullet\) False | |
\(\bullet\) Random_state | \(\bullet\) None | |
Decision Tree | \(\bullet\) Criterion | \(\bullet\) Gini |
\(\bullet\) Splitter | \(\bullet\) Best | |
\(\bullet\) Max_Depth | \(\bullet\) None | |
\(\bullet\) Minimum_samples_split | \(\bullet\) 2 | |
\(\bullet\) Minimum_samples_leaf | \(\bullet\) 1 | |
\(\bullet\) Minimum_weight_fraction_leaf | \(\bullet\) 0 | |
\(\bullet\) Max_features | \(\bullet\) None | |
\(\bullet\) Random_state | \(\bullet\) None | |
\(\bullet\) Minimum_impurity_decrease | \(\bullet\) 0 | |
\(\bullet\) Maximum_leaf_nodes | \(\bullet\) None | |
\(\bullet\) Random_state | \(\bullet\) None | |
Random Forest | \(\bullet\) N-estimators | \(\bullet\) 100 |
\(\bullet\) Criterion | \(\bullet\) Gini | |
\(\bullet\) Max_Depth | \(\bullet\) None | |
\(\bullet\) Minimum_samples_split | \(\bullet\) 2 | |
\(\bullet\) Minimum_samples_leaf | \(\bullet\) 1 | |
\(\bullet\) Minimum_weight_fraction_leaf | \(\bullet\) 0 | |
\(\bullet\) Max_features | \(\bullet\) None | |
\(\bullet\) Random_state | \(\bullet\) 0 | |
\(\bullet\) Minimum_impurity_decrease | \(\bullet\) 0 | |
\(\bullet\) Maximum_leaf_nodes | \(\bullet\) None | |
\(\bullet\) BootStrap | \(\bullet\) True | |
\(\bullet\) oob_score | \(\bullet\) False | |
\(\bullet\) n_jobs | \(\bullet\) 0 | |
\(\bullet\) Verbrose | \(\bullet\) None | |
\(\bullet\) Class_weight | \(\bullet\) 0 |
