newbie on machine learning

Hello all, So I have used 4 different machine learning algorithms in python’s scikit learn(listed below with python code I used to initialize each algorithm): Support Vector Machine alg = SVC() Random Forests alg = RandomForestClassifier(random_state = 1, n_estimators = 50, min_samples_split=4,min_samples_leaf = 2) Logistical Regression alg = LogisticRegression(random_state =1,solver = ‘lbfgs’) Naive Bayes alg_2 = GaussianNB() As you can see, I pretty much have used each algorithm right out of the box, without changing their parameters. As I sort of would expect, the best score I got was ~35% accuracy (using the SVM), when I used 3 folds in my cross validation scoring. I used the features ‘DayOfWeek’, ‘PdDistrict’, ‘Resolution’, ‘X’, ‘Y’. My question (finally) is: Are these algorithms the ones I should be using for this kind of…


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