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I am building a random forest classifier for DoS/DDoS attack detection, it is a binary classification problem. While tuning the parameters I got confused about what parameters to focus on exactly and what values I should choose. I chose some parameters as shown in the code, but I kept thinking what if I didn't include that one value that would give me the best result.

So my question is, from all the random forest's parameters, what parameters to focus on and which values I should give them?

#hyperparametres tuning
#finding best params
   forest = RandomForestClassifier()
   params = { 
                'n_estimators': [100,150,200],
                 'max_features': ['auto','log2'],
                 'max_depth' : [4,5,6,7],
                 'min_samples_split' :[2,3,4,5,6],
                 'max_samples': [100,150,200,250]
            }
   CV_rfc = GridSearchCV(estimator=forest, param_grid=params, cv= 5)
   CV_rfc.fit(x_train, y_train)
   CV_rfc.best_params_
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