020. classifier(svm, random forest, etc) # @ # I can see various kind of classifiers by searching "scikit-learn classifier" # If data can be separated by linear line, you can use svm # On the contrary, if data can't be separated by linear line # or if data can be separated by circle line # or if data can be separated by grid line, # you should use other methods # You can see pictures here # scikit-learn.org/stable/_images/sphx_glr_plot_classifier_comparison_001.png # scikit-learn.org/stable/auto_example/classification/plot_classifier_comparison.html # @ # You can test all methods with proper amount of data # and choose method which gives highest performance and precision # @ # Let's talk about random forest # Random forest creates randomly generated forest # at every running time into output result # So, whenever you run 'this random forest model', # accuracy can be changed a little bit import pandas as pd from sklearn.ensemble import RandomForestClassifier # For learning clf = RandomForestClassifier() clf.fit(train_data, train_label) # For predicting predict = clf.predict(test_data)