ML algorithms	Parameters
Logistic Regression	params = {  Logistic Regression:   norm of the penalty: [“l1”, “l2”, “elasticnet”] }
Random Forest	params = {  number of trees in the forest: [10, 100, 500, 1000],  maxim features at each split: [2, 4, 6, 8],  function to measure the quality of a split: [“gini”, “entropy”] }
Extra Trees	params = {  number of trees in the forest: [50, 500, 1000, 5000],  number of features to consider when looking for the best split: {“sqrt”, “log2”, None},  function to measure the quality of a split: [“gini”, “entropy”] }
XGBoost	params = {  minimum sum of instance weight (hessian) needed in a child: [1, 5, 10],  minimum loss reduction required to make a further partition on a leaf node of the tree(Gamma): [0.5, 1, 1.5, 2, 5],  subsample ratio of the training instances: [1.0, 0.8, 0.6],  subsample ratio of columns when constructing each tree: [0.6, 0.8, 1.0], maximum depth: [3, 4, 5]   }
AdaBoost	params={  maximum number of estimators at which boosting is terminated: [500, 1000, 2000, 5000],  Weight applied to each classifier at each boosting iteration (learning_rate): [.001,0.01,.1]   }
Support Vector Machine	params = {  kernel: [“poly”, “linear”, “rbf”, “sigmoid”],  Degree of the polynomial kernel function: [2, 3, 4, 5, 10],  Kernel coefficient for ‘rbf’, ‘poly’ and ‘sigmoid’: [“scale”, “auto”] }